├── LICENCE ├── README.md ├── applications.md ├── awesome_projects.md ├── books.md ├── corpus.md ├── courses.md ├── model_zoo.md ├── papers ├── 2016 │ ├── cv.md │ ├── dl.md │ ├── nlp.md │ └── rl.md ├── 2017 │ ├── cv.md │ ├── dl.md │ ├── nlp.md │ └── rl.md ├── 2018 │ ├── cv.md │ ├── dl.md │ ├── nlp.md │ └── rl.md ├── 2019 │ ├── cv.md │ ├── dl.md │ ├── nlp.md │ └── rl.md ├── 2020 │ ├── cv.md │ ├── dl.md │ ├── nlp.md │ └── rl.md ├── 2021 │ ├── cv.md │ ├── mm.md │ └── nlp.md ├── 2010.md ├── 2011.md ├── 2012.md ├── 2013.md ├── 2014.md ├── 2015.md └── before-2010.md ├── pre_trained.md ├── software.md └── tutorials.md /LICENCE: -------------------------------------------------------------------------------- 1 | The MIT License (MIT) 2 | 3 | Copyright (c) 2017-2018 endymecy and other contributors to this list. 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Awesome Deep learning papers and other resources 2 | 3 | ![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg) 4 | 5 | A list of recent papers regarding deep learning and deep reinforcement learning. They are sorted by time to see the recent papers first. 6 | I will renew the recent papers and add notes to these papers. 7 | 8 | You should find the papers and software with star flag are more important or popular. 9 | 10 | ## Table of Contents 11 | 12 | - [Papers](#papers) 13 | - [Model Zoo](#model-zoo) 14 | - [Pretrained Model](#pre-trained-model) 15 | - [Courses](#courses) 16 | - [Books](#books) 17 | - [Tutorials](#tutorials) 18 | - [Software](#software) 19 | - [Applications](#applications) 20 | - [Awesome Projects](#awesome-projects) 21 | - [Corpus](#corpus) 22 | 23 | # Papers 24 | - [2021 year](papers/2021/cv.md) 25 | - [computer vision](papers/2021/cv.md) 26 | - [natural language process](papers/2021/nlp.md) 27 | - [multi model](papers/2021/mm.md) 28 | - [2020 year](papers/2020/dl.md) 29 | - [deep learning](papers/2020/dl.md) 30 | - [deep reinforcement learning](papers/2020/rl.md) 31 | - [natural language process](papers/2020/nlp.md) 32 | - [computer vision](papers/2020/cv.md) 33 | - [2019 year](papers/2019/dl.md) 34 | - [deep learning](papers/2019/dl.md) 35 | - [deep reinforcement learning](papers/2019/rl.md) 36 | - [natural language process](papers/2019/nlp.md) 37 | - [computer vision](papers/2019/cv.md) 38 | - [2018 year](papers/2018/dl.md) 39 | - [deep learning](papers/2018/dl.md) 40 | - [deep reinforcement learning](papers/2018/rl.md) 41 | - [natural language process](papers/2018/nlp.md) 42 | - [computer vision](papers/2018/cv.md) 43 | - [2017 year](papers/2017/dl.md) 44 | - [deep learning](papers/2017/dl.md) 45 | - [deep reinforcement learning](papers/2017/rl.md) 46 | - [natural language process](papers/2017/nlp.md) 47 | - [computer vision](papers/2017/cv.md) 48 | - [2016 year](papers/2016/dl.md) 49 | - [deep learning](papers/2016/dl.md) 50 | - [deep reinforcement learning](papers/2016/rl.md) 51 | - [natural language process](papers/2016/nlp.md) 52 | - [computer vision](papers/2016/cv.md) 53 | - [2015 year](papers/2015.md) 54 | - [2014 year](papers/2014.md) 55 | - [2013 year](papers/2013.md) 56 | - [2012 year](papers/2012.md) 57 | - [2011 year](papers/2011.md) 58 | - [2010 year](papers/2010.md) 59 | - [before 2010 year](papers/before-2010.md) 60 | 61 | # Model Zoo 62 | 63 | * 2012 | AlexNet: ImageNet Classification with Deep Convolutional Neural Networks. [`pdf`](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf) [`code`](https://github.com/kratzert/finetune_alexnet_with_tensorflow) 64 | * 2013 | RCNN: Rich feature hierarchies for accurate object detection and semantic segmentation. [`arxiv`](https://arxiv.org/abs/1311.2524) [`code`](https://github.com/rbgirshick/rcnn) 65 | * 2014 | CGNA: Conditional Generative Adversarial Nets. [`arxiv`](https://arxiv.org/abs/1411.1784) [`code`](https://github.com/zhangqianhui/Conditional-Gans) 66 | * 2014 | DeepFaceVariant: Deep Learning Face Representation from Predicting 10,000 Classes. [`pdf`](http://mmlab.ie.cuhk.edu.hk/pdf/YiSun_CVPR14.pdf) [`code`](https://github.com/joyhuang9473/deepid-implementation) 67 | * 2014 | GAN: Generative Adversarial Networks. [`arxiv`](https://arxiv.org/abs/1406.2661) [`code`](https://github.com/goodfeli/adversarial) 68 | * 2014 | GoogLeNet: Going Deeper with Convolutions. [`pdf`](https://www.cs.unc.edu/~wliu/papers/GoogLeNet.pdf) [`code`](https://github.com/google/inception) 69 | 70 | More details in [Model Zoo](model_zoo.md) 71 | 72 | # Pre Trained Model 73 | 74 | * [Aligning the fastText vectors of 78 languages](https://github.com/Babylonpartners/fastText_multilingual) 75 | * [Available pretrained word embeddings](https://github.com/vzhong/embeddings) 76 | * [Inception-v3 of imagenet](http://download.tensorflow.org/models/image/imagenet/inception-v3-2016-03-01.tar.gz) 77 | * [Caffe2 Model Repository](https://github.com/caffe2/models) 78 | 79 | More details in [Pretrained Model](pre_trained.md) 80 | 81 | # Courses 82 | 83 | * [Berkeley] [CS294: Deep Reinforcement Learning](http://rll.berkeley.edu/deeprlcourse/) 84 | * [Berkeley] [Stat212b：Topics Course on Deep Learning](http://joanbruna.github.io/stat212b/) 85 | * [CUHK] [ELEG 5040: Advanced Topics in Signal Processing(Introduction to Deep Learning)](https://piazza.com/cuhk.edu.hk/spring2015/eleg5040/home) 86 | * [CMU] [Deep Reinforcement Learning and Control](https://katefvision.github.io/) 87 | * [CMU] [Neural networks for NLP](http://phontron.com/class/nn4nlp2017/) 88 | ) 89 | 90 | More details in [courses](courses.md) 91 | 92 | # Books 93 | 94 | * [Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville](http://www.deeplearningbook.org/). [`中文版本`](https://github.com/exacity/deeplearningbook-chinese) 95 | * [Deep Learning Tutorial by LISA lab, University of Montreal](http://deeplearning.net/tutorial/deeplearning.pdf) 96 | * [Deep Learning Crash Course](https://www.manning.com/livevideo/deep-learning-crash-course) 97 | * [Documentation on all topics that I learn on both Artificial intelligence and machine learning.](https://leonardoaraujosantos.gitbooks.io/artificial-inteligence/content/) 98 | * [Interpretable Machine Learning](https://christophm.github.io/interpretable-ml-book/) 99 | * [Deep Learning and the Game of Go](https://www.manning.com/books/deep-learning-and-the-game-of-go) 100 | * [Deep Learning for Search](https://www.manning.com/books/deep-learning-for-search) 101 | * [Deep Learning with PyTorch](https://www.manning.com/books/deep-learning-with-pytorch) 102 | * [Deep Reinforcement Learning in Action](https://www.manning.com/books/deep-reinforcement-learning-in-action) 103 | * [Grokking Deep Reinforcement Lerning](https://www.manning.com/books/grokking-deep-reinforcement-learning) 104 | * [Grokking Deep Learning for Computer Vision](https://www.manning.com/books/grokking-deep-learning-for-computer-vision) 105 | * [Probabilistic Deep Learning with Python](https://www.manning.com/books/probabilistic-deep-learning-with-python) 106 | * [Math and Architectures of Deep Learning](https://www.manning.com/books/math-and-architectures-of-deep-learning) 107 | * [Inside Deep Learning](https://www.manning.com/books/inside-deep-learning) 108 | * [Engineering Deep Learning Platforms](https://www.manning.com/books/engineering-deep-learning-platforms) 109 | * [Deep Learning with R, Second Edition](https://www.manning.com/books/deep-learning-with-r-second-edition) 110 | * [Regularization in Deep Learning](https://www.manning.com/books/regularization-in-deep-learning) 111 | * [Jax in Action](https://www.manning.com/books/jax-in-action) 112 | * [Deep Learning with PyTorch, Second Edition](https://www.manning.com/books/deep-learning-with-pytorch-second-edition) 113 | 114 | More details in [books](books.md) 115 | 116 | # Tutorials 117 | 118 | * [UFLDL Tutorial 1](http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial) 119 | * [UFLDL Tutorial 2](http://ufldl.stanford.edu/tutorial/supervised/LinearRegression/) 120 | * [Deep Learning for NLP (without Magic)](http://www.socher.org/index.php/DeepLearningTutorial/DeepLearningTutorial) 121 | * [A Deep Learning Tutorial: From Perceptrons to Deep Networks](http://www.toptal.com/machine-learning/an-introduction-to-deep-learning-from-perceptrons-to-deep-networks) 122 | * [Deep Learning from the Bottom up](http://www.metacademy.org/roadmaps/rgrosse/deep_learning) 123 | * [Theano Tutorial](http://deeplearning.net/tutorial/deeplearning.pdf) 124 | * [TensorFlow tutorials](https://github.com/nlintz/TensorFlow-Tutorials) 125 | * [Deep Learning with R in Motion](https://www.manning.com/livevideo/deep-learning-with-r-in-motion) 126 | * [Grokking Deep Learning in Motion](https://www.manning.com/livevideo/grokking-deep-learning-in-motion) 127 | * [Machine Learning, Data Science and Deep Learning with Python](https://www.manning.com/livevideo/machine-learning-data-science-and-deep-learning-with-python) 128 | 129 | More details in [tutorials](tutorials.md) 130 | 131 | # Software 132 | 133 | - `Keras` [Deep Learning library for Theano and TensorFlow.](https://keras.io/) :star: 134 | - `Kur` [Descriptive Deep Learning.](https://github.com/deepgram/kur) :star: 135 | - `Caffe` [Deep learning framework by the BVLC](http://caffe.berkeleyvision.org/) :star: 136 | - `CNTK` [The Microsoft Cognitive Toolkit.](https://github.com/Microsoft/CNTK) 137 | - `Dlib` [A modern C++ toolkit containing machine learning algorithms and tools for creating complex software in C++.](http://dlib.net/) 138 | - `PyTorch` [Tensors and Dynamic neural networks in Python with strong GPU acceleration.](http://pytorch.org/) :star: 139 | - `Scikit-Learn` [Machine learning in Python.](http://scikit-learn.org) :star: 140 | - `Semisup-Learn` [Semi-supervised learning frameworks for Python](https://github.com/tmadl/semisup-learn) 141 | - `Tensorflow` [An open source software library for numerical computation using data flow graph by Google](https://www.tensorflow.org/) :star: 142 | 143 | More details in [software](software.md) 144 | 145 | # Applications 146 | 147 | - pytorch 148 | - [2D and 3D Face alignment library build using pytorch](https://github.com/1adrianb/face-alignment) 149 | - [Adversarial Autoencoders](https://github.com/fducau/AAE_pytorch) 150 | - [A implementation of WaveNet with fast generation](https://github.com/vincentherrmann/pytorch-wavenet) 151 | - [A fast and differentiable QP solver for PyTorch.](https://github.com/locuslab/qpth) 152 | - [A method to generate speech across multiple speakers](https://github.com/facebookresearch/loop) 153 | - [A model for style-specific music generation](https://github.com/calclavia/DeepJ) :star: 154 | - [A natural language processing toolkit using state-of-the-art deep learning models.](https://github.com/allenai/allennlp) :star: 155 | - [使用PyTorch实现Char RNN生成古诗和周杰伦的歌词](https://github.com/SherlockLiao/Char-RNN-PyTorch) 156 | - theano 157 | - [CNN-yelp-challenge-2016-sentiment-classification](https://github.com/haoopeng/CNN-yelp-challenge-2016-sentiment-classification) 158 | - [Deep learning tutorial for PyData](https://github.com/Britefury/deep-learning-tutorial-pydata) 159 | - [Deep Neural Network for Sentiment Analysis on Twitter](https://github.com/xiaohan2012/twitter-sent-dnn) 160 | - [Implementations of many popular deep learning models in Theano+Lasagne](https://github.com/kuleshov/deep-learning-models) 161 | - tensorflow 162 | - [A generic image detection program that uses tensorflow and a pre-trained Inception.](https://github.com/ArunMichaelDsouza/tensorflow-image-detection) 163 | - [All kinds of text classificaiton models and more with deep learning](https://github.com/brightmart/text_classification) :star: 164 | - [Applying transfer learning to a custom dataset by retraining Inception's final layer](https://github.com/HusainZafar/TransferLearningTutorial) 165 | - [An easy implement of VGG19 with tensorflow, which has a detailed explanation.](https://github.com/hjptriplebee/VGG19_with_tensorflow) 166 | - [An experimentation system for Reinforcement Learning using OpenAI Gym, Tensorflow, and Keras.](https://github.com/kengz/openai_lab) :star: 167 | - [An implementation of Pix2Pix in Tensorflow for use with frames from films](https://github.com/awjuliani/Pix2Pix-Film) 168 | - Keras 169 | - [A DCGAN to generate anime faces using custom mined dataset](https://github.com/pavitrakumar78/Anime-Face-GAN-Keras) 170 | - [A facial expression classification system that recognizes 6 basic emotions: happy, sad, surprise, fear, anger and neutral.](https://github.com/JostineHo/mememoji) 171 | - [A neural network trained to help writing neural network code using autocomplete](https://github.com/kootenpv/neural_complete) 172 | - [Attention mechanism Implementation for Keras.](https://github.com/philipperemy/keras-attention-mechanism) 173 | - [Automated deep neural network design with genetic programming](https://github.com/joeddav/devol) :star: 174 | - [Attention based Neural Machine Translation for Keras](https://github.com/divamgupta/attention-translation-keras) 175 | - [Keras Implementation of Ladder Network for Semi-Supervised Learning](https://github.com/divamgupta/ladder_network_keras) 176 | 177 | - Mxnet 178 | - [使用MXNet的动态图接口Gluon实现Char RNN生成古诗和周杰伦的歌词](https://github.com/SherlockLiao/Char-RNN-Gluon) 179 | 180 | More details in [applications](applications.md) 181 | 182 | # Awesome Projects 183 | 184 | - [15 AI and Machine Learning Events](http://botunity.co/14-ai-and-machine-learning-events/) 185 | - [188 examples of artificial intelligence in action](https://poo.ai/) 186 | - [A curated list of automated machine learning papers, articles, tutorials, slides and projects](https://github.com/hibayesian/awesome-automl-papers) :star: 187 | - [A curated list of awesome Machine Learning frameworks, libraries and software.](https://github.com/josephmisiti/awesome-machine-learning) 188 | - [A curated list of awesome places to learn and/or practice algorithms.](https://github.com/tayllan/awesome-algorithms) 189 | - [A curated list of awesome R packages and tools](https://github.com/qinwf/awesome-R) 190 | - [A curated list of awesome SLAM tutorials, projects and communities.](https://github.com/kanster/awesome-slam) 191 | - [A curated list of resources dedicated to bridge between coginitive science and deep learning](https://github.com/robi56/awesome-cognitive-science-and-deep-learning) 192 | - [A curated list of resources dedicated to Natural Language Processing (NLP)](https://github.com/keon/awesome-nlp) 193 | - [A curated list of resources for NLP (Natural Language Processing) for Chinese](https://github.com/crownpku/awesome-chinese-nlp#corpus-%E4%B8%AD%E6%96%87%E8%AF%AD%E6%96%99) 194 | - [Another curated list of deep learning resources](https://github.com/guillaume-chevalier/Awesome-Deep-Learning-Resources) 195 | - [A list of artificial intelligence tools you can use today](https://hackernoon.com/a-list-of-artificial-intelligence-tools-you-can-use-today-for-personal-use-1-3-7f1b60b6c94f) 196 | - [A list of deep learning implementations in biology](https://github.com/hussius/deeplearning-biology) 197 | - [Awesome-2vec](https://github.com/MaxwellRebo/awesome-2vec) 198 | - [Awesome Action Recognition](https://github.com/jinwchoi/awesome-action-recognition) 199 | 200 | More details in [awesome projects](awesome_projects.md) 201 | 202 | # Corpus 203 | 204 | - [用于对话系统的中英文语料](https://github.com/candlewill/Dialog_Corpus) 205 | - [搜狗实验室](http://www.sogou.com/labs/) 206 | - [情感分析︱网络公开的免费文本语料训练数据集汇总](http://blog.csdn.net/sinat_26917383/article/details/51321505) 207 | - [中文情感分析用词语集](http://www.keenage.com/html/c_bulletin_2007.htm) 208 | - [人民日报切分/标注语料库](http://www.icl.pku.edu.cn/icl_res/) 209 | - [哈工大信息检索研究中心(HIT CIR)语言技术平台共享资源](http://ir.hit.edu.cn/demo/ltp/Sharing_Plan.htm) 210 | - [中文句结构树资料库](http://turing.iis.sinica.edu.tw/treesearch/) 211 | - [中文对白语料 chinese conversation corpus](https://github.com/rustch3n/dgk_lost_conv) 212 | - [中文语料小数据：Some useful Chinese corpus datasets](https://github.com/crownpku/Small-Chinese-Corpus) 213 | - [中文人名语料库。中文姓名,姓氏,名字,称呼,日本人名,翻译人名,英文人名](https://github.com/wainshine/Chinese-Names-Corpus) 214 | - [联合国平行语料库](https://conferences.unite.un.org/UNCorpus/zh) 215 | - [保险行业语料库](https://github.com/Samurais/insuranceqa-corpus-zh) 216 | - [用于训练中英文对话系统的语料库 Datasets for Training Chatbot System](https://github.com/candlewill/Dialog_Corpus) 217 | - [PTT 八卦版問答中文語料](https://github.com/zake7749/Gossiping-Chinese-Corpus) 218 | - [3 Million Instacart Orders, Open Sourced](https://www.instacart.com/datasets/grocery-shopping-2017) 219 | - [ACM Multimedia Systems Conference Dataset Archive](http://traces.cs.umass.edu/index.php/Mmsys/Mmsys) 220 | - [A dataset for book recommendations: ten thousand books, one million ratings](https://www.kaggle.com/zygmunt/goodbooks-10k) 221 | - [A dataset for personalized highlight detection](https://github.com/gifs/personalized-highlights-dataset) 222 | - [A dataset of 200k English plaintext jokes.](https://github.com/taivop/joke-dataset) 223 | - [A large-scale and high-qualityFMA: A Dataset For Music Analysis dataset of annotated musical notes.](https://magenta.tensorflow.org/datasets/nsynth) 224 | - [A large-scale dataset of manually annotated audio events](https://research.google.com/audioset/) :star: 225 | - [Alphabetical list of free/public domain datasets with text data for use in NLP](https://github.com/niderhoff/nlp-datasets) 226 | 227 | More details in [corpus](corpus.md) 228 | 229 | # Other Resources 230 | 231 | - [Synthical](https://synthical.com) - AI-powered collaborative research environment. You can use it to get recommendations of articles based on reading history, simplify papers, find out what articles are trending, search articles by meaning (not just keywords), create and share folders of articles, see lists of articles from specific companies and universities, and so on. 232 | 233 | # Contributors 234 | 235 | Special thanks to everyone who contributed to this project. 236 | 237 | - raer6 238 | - isikdogan 239 | - outlace 240 | - divamgupta 241 | - Naman-Bhalla 242 | - ppuliu 243 | - benedekrozemberczki 244 | - roziunicorn 245 | - von-latinski 246 | 247 | # License 248 | 249 | The details in [License](LICENCE) 250 | -------------------------------------------------------------------------------- /awesome_projects.md: -------------------------------------------------------------------------------- 1 | # Awesome Projects 2 | 3 | - [15 AI and Machine Learning Events](http://botunity.co/14-ai-and-machine-learning-events/) 4 | - [188 examples of artificial intelligence in action](https://poo.ai/) 5 | - [2018-2019 International Conferences in Artificial Intelligence, Computer Vision and Image Processing](https://github.com/JackieTseng/conference_call_for_paper) 6 | - [A collection of AWESOME things about domian adaptation](https://github.com/zhaoxin94/awsome-domain-adaptation) 7 | - [A collection of datasets ready to use with TensorFlow](https://github.com/tensorflow/datasets) 8 | - [A collection of important graph embedding, classification and representation learning](https://github.com/benedekrozemberczki/awesome-graph-classification) 9 | - [A collection of methods to fool the deep neural network](https://github.com/layumi/Awesome-Fools) 10 | - [A curated collection of places where you can learn robotics, algorithms, and other useful tools for aspiring robotics software engineers.](https://github.com/mithi/robotics-coursework) 11 | - [A curated list of automated machine learning papers, articles, tutorials, slides and projects](https://github.com/hibayesian/awesome-automl-papers) :star: 12 | - [A curated list of awesome anomaly detection resources](https://github.com/hoya012/awesome-anomaly-detection) 13 | - [A curated list of awesome computer vision resources](https://github.com/jbhuang0604/awesome-computer-vision) :star: 14 | - [A curated list of awesome Machine Learning frameworks, libraries and software.](https://github.com/josephmisiti/awesome-machine-learning) 15 | - [A curated list of awesome places to learn and/or practice algorithms.](https://github.com/tayllan/awesome-algorithms) 16 | - [A curated list of awesome resources related to capsule networks](https://github.com/aisummary/awesome-capsule-networks) 17 | - [A curated list of awesome R packages and tools](https://github.com/qinwf/awesome-R) 18 | - [A curated list of awesome SLAM tutorials, projects and communities.](https://github.com/kanster/awesome-slam) 19 | - [A list of papers on Generative Adversarial (Neural) Networks](https://github.com/nightrome/really-awesome-gan) :star: 20 | - [A collection of TensorFlow.js projects, tutorials, videos, and more.](https://github.com/tensorflow/tfjs/blob/master/GALLERY.md) 21 | - [A compilation of the list of top algorithms tweeted here](https://mathematical-tours.github.io/algorithms/) 22 | - [A curated list of neural network pruning resources.](https://github.com/he-y/Awesome-Pruning) 23 | - [A curated list of resources dedicated to bridge between coginitive science and deep learning](https://github.com/robi56/awesome-cognitive-science-and-deep-learning) 24 | - [A curated list of resources dedicated to Natural Language Processing (NLP)](https://github.com/keon/awesome-nlp) :star: 25 | - [A curated list of resources for Chinese NLP](https://github.com/crownpku/awesome-chinese-nlp#corpus-%E4%B8%AD%E6%96%87%E8%AF%AD%E6%96%99) 26 | - [A curated list of robotics libraries and software](https://github.com/jslee02/awesome-robotics-libraries) 27 | - [A list of artificial intelligence tools you can use today](https://hackernoon.com/a-list-of-artificial-intelligence-tools-you-can-use-today-for-personal-use-1-3-7f1b60b6c94f) 28 | - [A list of deep learning implementations in biology](https://github.com/hussius/deeplearning-biology) 29 | - [Alphabetical list of free/public domain datasets with text data for use in Natural Language Processing](https://github.com/niderhoff/nlp-datasets) 30 | - [A Knowledge Base for the FB Group Artificial Intelligence and Deep Learning (AIDL)](https://github.com/arthchan2003/AIDL_KB) 31 | - [An awesome Data Science repository to learn and apply for real world problems.](https://github.com/bulutyazilim/awesome-datascience) 32 | - [A Paper List for Style Transfer in Text](https://github.com/fuzhenxin/Style-Transfer-in-Text) 33 | - [A reading list of top Conferences & Journals, focused on facial expression recognition](https://github.com/EvelynFan/AWESOME-FER) 34 | - [Awesome-2vec](https://github.com/MaxwellRebo/awesome-2vec) 35 | - [Awesome Action Recognition](https://github.com/jinwchoi/awesome-action-recognition) 36 | - [Awesome Active Learning](https://github.com/baifanxxx/awesome-active-learning) 37 | - [Awesome Adversarial Machine Learning](https://github.com/yenchenlin/awesome-adversarial-machine-learning) 38 | - [Awesome AI Security](https://github.com/RandomAdversary/Awesome-AI-Security) 39 | - [Awesome ARKit](https://github.com/olucurious/awesome-arkit) 40 | - [Awesome Autonomous Vehicles](https://github.com/takeitallsource/awesome-autonomous-vehicles) 41 | - [Awesome bayesian deep learning](https://github.com/robi56/awesome-bayesian-deep-learning) 42 | - [Awesome Chatbot Projects,Corpus,Papers,Tutorials.](https://github.com/fendouai/Awesome-Chatbot) 43 | - [Awesome Deep Learning for Natural Language Processing](https://github.com/brianspiering/awesome-dl4nlp) 44 | - [Awesome deep vision web demo](https://github.com/hwalsuklee/awesome-deep-vision-web-demo) 45 | - [Awesome Caffe](https://github.com/MichaelXin/Awesome-Caffe) 46 | - [Awesome Chatbot Projects,Corpus,Papers,Tutorials.](https://github.com/fendouai/Awesome-Chatbot) 47 | - [Awesome Decision Trees](https://github.com/benedekrozemberczki/awesome-decision-tree-papers) 48 | - [Awesome Deep Learning](https://github.com/ChristosChristofidis/awesome-deep-learning) 49 | - [Awesome Deep learning papers and other resources](https://github.com/endymecy/awesome-deeplearning-resources) 50 | - [Awesome Deep Learning Resources](https://github.com/guillaume-chevalier/awesome-deep-learning-resources) 51 | - [Awesome Deep Vision](https://github.com/kjw0612/awesome-deep-vision) 52 | - [Awesome Document Similarity Measures](https://github.com/malteos/awesome-document-similarity) 53 | - [Awesome Explorables](https://github.com/sp4ke/awesome-explorables) 54 | - [Awesome Fraud Detection Research Papers.](https://github.com/benedekrozemberczki/awesome-fraud-detection-papers) 55 | - [Awesome GAN for Medical Imaging](https://github.com/xinario/awesome-gan-for-medical-imaging) 56 | - [Awesome Generative Adversarial Networks with tensorflow](https://github.com/kozistr/Awesome-GANs) 57 | - [Awesome Gradient Boosting](https://github.com/benedekrozemberczki/awesome-gradient-boosting-papers) 58 | - [Awesome Human Pose Estimation](https://github.com/wangzheallen/awesome-human-pose-estimation) 59 | - [Awesome Image Inpainting](https://github.com/1900zyh/Awesome-Image-Inpainting) 60 | - [Awesome Knowledge Distillation](https://github.com/dkozlov/awesome-knowledge-distillation) 61 | - [Awesome Law NLP Research Work](https://github.com/bamtercelboo/Awesome-Law-NLP-Research-Work) 62 | - [Awesome Machine Learning On Source Code](https://github.com/src-d/awesome-machine-learning-on-source-code) 63 | - [Awesome Machine Learning Projects](https://ml-showcase.com/) 64 | - [Awesome Math](https://github.com/llSourcell/learn_math_fast) 65 | - [Awesome Monte Carlo Tree Search Papers](https://github.com/benedekrozemberczki/awesome-monte-carlo-tree-search-papers) 66 | - [Awesome Most Cited Deep Learning Papers](https://github.com/terryum/awesome-deep-learning-papers) 67 | - [Awesome MXNet](https://github.com//chinakook/Awesome-MXNet) 68 | - [Awesome Network Embedding](https://github.com/chihming/awesome-network-embedding) 69 | - [Awesome Object Detection](https://github.com/amusi/awesome-object-detection) 70 | - [Awesome Person Re-identification (Person ReID)](https://github.com/bismex/Awesome-person-re-identification) 71 | - [Awesome Public Datasets](https://github.com/caesar0301/awesome-public-datasets) 72 | - [Awesome PyTorch](https://github.com/rickiepark/awesome-pytorch) :star: 73 | - [Awesome-question-answering](https://github.com/dapurv5/awesome-question-answering) 74 | - [Awesome Reinforcement Learning](https://github.com/aikorea/awesome-rl) :star: 75 | - [Awesome Robotics](https://github.com/Kiloreux/awesome-robotics) 76 | - [Awesome Sentiment Analysis](https://github.com/xiamx/awesome-sentiment-analysis) 77 | - [Awesome-SLAM-list](https://github.com/OpenSLAM/awesome-SLAM-list) 78 | - [Awesome Semantic Segmentation](https://github.com/mrgloom/awesome-semantic-segmentation) 79 | - [Awesome speech](https://github.com/mxer/awesome-speech) :star: 80 | - [Awesome speech recognition papers](https://github.com/zzw922cn/awesome-speech-recognition-papers) 81 | - [Awesome speech recognition speech synthesis papers](https://github.com/zzw922cn/awesome-speech-recognition-speech-synthesis-papers) 82 | - [Awesome Super Resolution](https://github.com/ptkin/Awesome-Super-Resolution) 83 | - [Awesome TensorFlow](https://github.com/jtoy/awesome-tensorflow) :star: 84 | - [Chainer Info](https://github.com/hidetomasuoka/chainer-info) 85 | - [Chatbot and Related Research Paper Notes with Images](https://github.com/ricsinaruto/Seq2seqChatbots/wiki/Chatbot-and-Related-Research-Paper-Notes-with-Images) 86 | - [Chat corpus collection from various open sources](https://github.com/Marsan-Ma/chat_corpus) 87 | - [Collection of generative models, e.g. GAN, VAE in Tensorflow, Keras, and Pytorch](https://github.com/wiseodd/generative-models) :star: 88 | - [Collection of Keras models used for classification](https://github.com//titu1994/Keras-Classification-Models) 89 | - [Collection of papers and other resources for object tracking and detection using deep learning](https://github.com/abhineet123/Deep-Learning-for-Tracking-and-Detection) 90 | - [Collection of reinforcement learners implemented in python.](https://github.com/Islandman93/reinforcepy) 91 | - [Core ML Models](https://github.com/likedan/Awesome-CoreML-Models) 92 | - [Curated list of python software and packages related to scientific research in audio](https://github.com/faroit/awesome-python-scientific-audio) 93 | - [Datasets for Natural Language Processing](https://github.com/karthikncode/nlp-datasets) 94 | - [Datasets, Transforms and Models specific to Computer Vision](https://github.com/pytorch/vision/) 95 | - [Deep Learning and applications in Startups, CV, Text Mining, NLP](https://github.com/lipiji/App-DL) 96 | - [Deep Learning and Time Series](https://github.com//FrancisArgnR/Time-series---deep-learning---state-of-the-art) 97 | - [Deep Learning for Recommendation Systems](https://github.com/robi56/Deep-Learning-for-Recommendation-Systems) 98 | - [Deep Learning Model Convertors](https://github.com/ysh329/deep-learning-model-convertor) 99 | - [Deep Learning Papers Reading Roadmap](https://github.com/songrotek/Deep-Learning-Papers-Reading-Roadmap) 100 | - [Deep Learning Papers Translation](https://github.com/SnailTyan/deep-learning-papers-translation) 101 | - [Deep Learning Processor List](https://zhuanlan.zhihu.com/p/28406226) 102 | - [Deep Learning Toolbox](https://github.com/dmarnerides/dlt) 103 | - [Enumerate diverse machine learning training tricks.](https://github.com/Conchylicultor/Deep-Learning-Tricks) 104 | - [First International Workshop on Deep Learning and Music](https://arxiv.org/html/1706.08675) 105 | - [Generative Models](https://github.com/wiseodd/generative-models) 106 | - [Graph Convolutional Networks (GCNs)](https://github.com/sungyongs/graph-based-nn) 107 | - [HEPML Resources](https://github.com/iml-wg/HEP-ML-Resources) 108 | - [Implementing nlp papers with PyTorch, gluonnlp](https://github.com/aisolab/nlp_implementation) 109 | - [Knowledge distillation papers](https://github.com/lhyfst/knowledge-distillation-papers) 110 | - [List of articles related to deep learning applied to music](https://github.com/ybayle/awesome-deep-learning-music) 111 | - [List of Recommender Systems](https://github.com/grahamjenson/list_of_recommender_systems) :star: 112 | - [Machine Learning / Deep Learning Conferences](https://tryolabs.com/blog/machine-learning-deep-learning-conferences/) 113 | - [Machine Learning Videos](https://github.com/dustinvtran/ml-videos) 114 | - [Machine Learning From Scratch](https://github.com/eriklindernoren/ML-From-Scratch) 115 | - [Must-read papers on NRL/NE.](https://github.com/thunlp/NRLpapers) 116 | - [Must-read papers on Recommender System](https://github.com/hongleizhang/RSPapers) 117 | - [Multi Object Tracking Paper List](https://github.com/SpyderXu/multi-object-tracking-paper-list) 118 | - [Natural language processing paper list](https://github.com/changwookjun/nlp-paper) 119 | - [Network acceleration methods](https://github.com/mrgloom/Network-Speed-and-Compression) 120 | - [Neural Networks on Silicon](https://github.com/fengbintu/Neural-Networks-on-Silicon) 121 | - [Notes/links on math and science, including statistics, bayes, cmpsc, quant trading, machine learning, etc](https://github.com/melling/MathAndScienceNotes) 122 | - [OCR Resources](https://github.com/ZumingHuang/awesome-ocr-resources) 123 | - [Open-source simulator for autonomous driving research](https://github.com/carla-simulator/carla) :star: 124 | - [Over 7 million published research papers in Computer Science and Neuroscience.](http://labs.semanticscholar.org/corpus/) 125 | - [Paper list of multi-agent reinforcement learning (MARL)](https://github.com/LantaoYu/MARL-Papers) 126 | - [Papers with code](https://github.com/zziz/pwc) :star: 127 | - [Private machine learning progress](https://github.com/OpenMined/awesome-ai-privacy) 128 | - [Really-awesome-gan](https://github.com/nightrome/really-awesome-gan) 129 | - [Really-awesome-semantic-segmentation](https://github.com/nightrome/really-awesome-semantic-segmentation) 130 | - [Recommendation Algorithms](https://github.com/chihming/competitive-recsys) 131 | - [Recommended Papers. Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Learning (cs.LG)](https://github.com//ArcherFMY/Paper_Reading_List) 132 | - [Related Paper of Efficient Deep Neural Networks](https://github.com/Zhouaojun/Efficient-Deep-Learning) 133 | - [Repository to research & share the machine learning articles](https://github.com/arXivTimes/arXivTimes) 134 | - [Resources and codes about transfer learning and domain adaptation](https://github.com/jindongwang/transferlearning) 135 | - [SCODE Word Embeddings using Substitute Words](https://github.com/ai-ku/wvec) 136 | - [Semi-Supervised Video Object Segmentation](https://github.com/du0915/Video-Object-Segmentation-Paper-List) 137 | - [Some good resources for NNMT](http://thegrandjanitor.com/2017/09/09/some-useful-links-on-neural-machine-translation/) :star: 138 | - [Speech and Natural Language Processing](https://github.com/edobashira/speech-language-processing) 139 | - [Summaries and notes on Deep Learning research papers](https://github.com/dennybritz/deeplearning-papernotes) 140 | - [TensorFlow and Deep Learning Tutorials](https://github.com/wagamamaz/tensorflow-tutorial) 141 | - [Tensorflow implementations of Graph Neural Networks](https://github.com/microsoft/tf-gnn-samples) 142 | - [TensorFlow Tutorial and Examples for beginners](https://github.com/aymericdamien/TensorFlow-Examples) :star: 143 | - [TensorFlow World Resources](https://github.com/astorfi/TensorFlow-World-Resources) 144 | - [The awesome and classic papers in recommendation system](https://github.com/YuyangZhangFTD/awesome-RecSys-papers) 145 | - [The GAN Zoo](https://github.com/hindupuravinash/the-gan-zoo) 146 | - [The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch](https://github.com/ritchieng/the-incredible-pytorch) :star: 147 | - [The most cited deep learning papers](https://github.com/terryum/awesome-deep-learning-papers) :star: 148 | - [The project attempts to maintain the SOTA performance in machine translation](https://github.com/ZNLP/SOTA-MT) 149 | - [Using python to work with time series data](https://github.com/MaxBenChrist/awesome_time_series_in_python) 150 | - [Use PyTorch to implement some classic frameworks](https://github.com/sunshineatnoon/Paper-Implementations) 151 | - [Various math-related things in Python code](https://github.com/calebmadrigal/math-with-python) 152 | - [Video Super Resolution](https://github.com/flyywh/Video-Super-Resolution) 153 | - [机器学习资源 Machine learning](https://github.com/allmachinelearning/MachineLearning) 154 | - [机器学习资源大全中文版](https://github.com/jobbole/awesome-machine-learning-cn) 155 | - [图像文本位置感知与识别的论文资源汇总](https://github.com/whitelok/image-text-localization-recognition/blob/master/README.zh-cn.md) 156 | - [深度学习进行目标识别的资源列表](http://www.thinkface.cn/thread-4434-1-1.html) 157 | - [文本摘要资源列表](https://github.com/mathsyouth/awesome-text-summarization) 158 | - [计算机视觉相关论文整理、翻译、记录、分享](https://github.com/yizt/cv-papers) :star: 159 | -------------------------------------------------------------------------------- /books.md: -------------------------------------------------------------------------------- 1 | # Books 2 | 3 | * [神经网络与深度学习.邱锡鹏](https://nndl.github.io/) 4 | * [深度学习入门 by PaddlePaddle](https://github.com/PaddlePaddle/book) 5 | * [动手学深度学习](https://github.com/d2l-ai/d2l-zh) 6 | * [Scikit-learn 秘籍](https://www.gitbook.com/book/wizardforcel/sklearn-cookbook/details) 7 | * [A Beginner's Guide to the Mathematics of Neural Networks](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.161.3556&rep=rep1&type=pdf) 8 | * [A Course in Machine Learning](http://ciml.info/) 9 | * [Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville](http://www.deeplearningbook.org/). [`中文版本`](https://github.com/exacity/deeplearningbook-chinese) 10 | * [Deep Learning Tutorial by LISA lab, University of Montreal](http://deeplearning.net/tutorial/deeplearning.pdf) 11 | * [Documentation on all topics that I learn on both Artificial intelligence and machine learning.](https://leonardoaraujosantos.gitbooks.io/artificial-inteligence/content/) 12 | * [Deep Reinforcement Learning in Action](https://www.manning.com/books/deep-reinforcement-learning-in-action) 13 | * [First Contact With TensorFlow](http://jorditorres.org/first-contact-with-tensorflow/) 14 | * [Interpretable Machine Learning](https://christophm.github.io/interpretable-ml-book/) 15 | * [Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python](https://github.com/rasbt/deep-learning-book) 16 | * [Learning scikit-learn: Machine Learning in Python](https://github.com/gmonce/scikit-learn-book) 17 | * [Mathematical Foundations](https://mathematical-tours.github.io/book/) 18 | * [Natural Language Processing in Action](https://www.manning.com/books/natural-language-processing-in-action) 19 | * [Neural Networks and Deep Learning by Michael Nielsen](http://neuralnetworksanddeeplearning.com/) 20 | * [Neural Networks: Tricks of the Trade (Lecture Notes in Computer Science)](https://link.springer.com/book/10.1007%2F978-3-642-35289-8) 21 | * [Python Data Science Handbook](https://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/Index.ipynb) 22 | * [Rules of Machine Learning: Best Practices for ML Engineering](http://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf) 23 | * [Reinforcement Learning: An Introduction](https://drive.google.com/file/d/1xeUDVGWGUUv1-ccUMAZHJLej2C7aAFWY/view). [`code`](https://github.com/ShangtongZhang/reinforcement-learning-an-introduction) 24 | * [Reinforcement LearningState-of-the-Art](http://www.ai.rug.nl/~mwiering/RL-state-of-the-art) 25 | * [Speech and Language Processing](http://web.stanford.edu/~jurafsky/slp3/) 26 | * [TensorFlow For Machine Intelligence](https://bleedingedgepress.com/tensor-flow-for-machine-intelligence/) 27 | * [The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation](https://www.eff.org/files/2018/02/20/malicious_ai_report_final.pdf) 28 | * [UFLDL Tutorial](http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial) 29 | -------------------------------------------------------------------------------- /corpus.md: -------------------------------------------------------------------------------- 1 | # Corpus 2 | 3 | - [数据堂](http://more.datatang.com/) 4 | - [语料库在线](http://www.cncorpus.org/index.aspx) 5 | - [3 Million Instacart Orders, Open Sourced](https://www.instacart.com/datasets/grocery-shopping-2017) 6 | - [ACM Multimedia Systems Conference Dataset Archive](http://traces.cs.umass.edu/index.php/Mmsys/Mmsys) 7 | - [A comprehensive dataset for stock movement prediction from tweets and historical stock prices.](https://github.com/yumoxu/stocknet-dataset) 8 | - [A dataset for book recommendations: ten thousand books, one million ratings](https://www.kaggle.com/zygmunt/goodbooks-10k) 9 | - [An awesome list of high-quality datasets](https://webhose.io/datasets) :star: 10 | - [An awesome list of high-quality open datasets in public domains](https://github.com/caesar0301/awesome-public-datasets) :star: 11 | - [A new dataset for Attribute Based Classification and Zero-Shot Learning](http://cvml.ist.ac.at/AwA2/) 12 | - [Audio Data Links](https://github.com/robmsmt/ASR_Audio_Data_Links) 13 | - [Clustering basic benchmark](https://cs.joensuu.fi/sipu/datasets/) 14 | - [CNSD 中文自然语言推理数据集](https://github.com/zengjunjun/CNSD) 15 | - [Cool Datasets](http://cooldataseWolfram Data Repositoryts.com/#Machine-Learning-Datasets) :star: 16 | - [Corpora of misspellings for download](http://www.dcs.bbk.ac.uk/~ROGER/corpora.html) 17 | - [DATASETS FOR DATA MINING](http://www.inf.ed.ac.uk/teaching/courses/dme/html/datasets0405.html) 18 | - [Datasets for Data Science and Machine Learning](https://elitedatascience.com/datasets) 19 | - [DeepDive Open Datasets](http://deepdive.stanford.edu/opendata/) :star: 20 | - [FiveThirtyEight开放可视化数据](https://www.weibo.com/fly51fly?is_all=1#_rnd1518270026401) 21 | - [Hard Drive Data and Stats](https://www.backblaze.com/b2/hard-drive-test-data.html) 22 | - [Open Datasets](https://skymind.ai/wiki/open-datasets) 23 | - [Picture and specifications scraper](https://github.com/nicolas-gervais/predicting-car-price-from-scraped-data) 24 | - [Pixiv Dataset Overview](https://github.com/jerryli27/pixiv_dataset) 25 | - [SLAC: A Sparsely Labeled ACtions Dataset from MIT and Facebook](http://slac.csail.mit.edu/) 26 | - [Some good papers I like](https://github.com/hoangcuong2011/Good-Papers) 27 | - [Standardized data set for machine learning of protein structure](https://github.com/aqlaboratory/proteinnet) 28 | - [Telenav.AI competition public repository](https://github.com/Telenav/Telenav.AI) 29 | - [The Quick, Draw! Dataset](https://github.com/googlecreativelab/quickdraw-dataset) 30 | - [Wolfram Data Repository](https://datarepository.wolframcloud.com/) 31 | 32 | 33 | ## CV 34 | 35 | - [300 Faces In-the-Wild Challenge](https://ibug.doc.ic.ac.uk/resources/300-W/) 36 | - [A dataset for personalized highlight detection](https://github.com/gifs/personalized-highlights-dataset) 37 | - [A Large-Scale Dataset for Vehicle Re-Identification in the Wild](https://github.com/PKU-IMRE/VERI-Wild) 38 | - [A MNIST-like fashion product database](https://github.com/zalandoresearch/fashion-mnist) :star: 39 | - [Caltech 10, 000 Web Faces](http://www.vision.caltech.edu/Image_Datasets/Caltech_10K_WebFaces/#Download) 40 | - [CASIA WebFace Database](http://www.cbsr.ia.ac.cn/english/CASIA-WebFace-Database.html) 41 | - [Cross-Age Celebrity Dataset](http://bcsiriuschen.github.io/CARC/) 42 | - [DeepFashion: Fashion Landmark Detection](http://mmlab.ie.cuhk.edu.hk/projects/DeepFashion/LandmarkDetection.html) 43 | - [EMOTIC Dataset](http://sunai.uoc.edu/emotic/) 44 | - [Face Recognition for Web-Scale Datasets](http://enriquegortiz.com/wordpress/enriquegortiz/research/face-recognition/webscale-face-recognition/) 45 | - [IMDB-WIKI – 500k+ face images with age and gender labels](https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/) 46 | - [Kaggle Datasets](https://www.kaggle.com/datasets) 47 | - [Labeled Faces in the Wild Home](http://vis-www.cs.umass.edu/lfw/) 48 | - [Large-scale CelebFaces Attributes (CelebA) Dataset](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) 49 | - [LLD - Large Logo Dataset](https://data.vision.ee.ethz.ch/cvl/lld/) 50 | - [Medical imaging datasets](https://github.com/sfikas/medical-imaging-datasets) 51 | - [Media Integration and Communication Center](https://www.micc.unifi.it/resources/) 52 | - [MegaFace Dataset](http://megaface.cs.washington.edu/dataset/download.html) 53 | - [MSRA-CFW: Data Set of Celebrity Faces on the Web](https://www.microsoft.com/en-us/research/project/msra-cfw-data-set-of-celebrity-faces-on-the-web/?from=http%3A%2F%2Fresearch.microsoft.com%2Fen-us%2Fprojects%2Fmsra-cfw%2Fcasia) 54 | - [Netizen-Style Commenting on Fashion Photos – Dataset and Diversity Measures](https://mashyu.github.io/NSC/) 55 | - [Open Images Dataset V4](https://storage.googleapis.com/openimages/web/index.html) 56 | - [SCUT HEAD is a large-scale head detection dataset](https://github.com/HCIILAB/SCUT-HEAD-Dataset-Release) 57 | - [Street View Image, Pose, and 3D Cities Dataset](https://github.com/amir32002/3D_Street_View) 58 | - [VGG Face Dataset](http://www.robots.ox.ac.uk/~vgg/data/vgg_face/) 59 | - [VGGFace2 Dataset](http://www.robots.ox.ac.uk/~vgg/data/vgg_face2/) 60 | - [WebVision视觉数据集2.0](http://www.vision.ee.ethz.ch/webvision/index.html) 61 | - [WIDER FACE: A Face Detection Benchmark](http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/) 62 | - [YouTube Faces DB](https://www.cs.tau.ac.il/~wolf/ytfaces/) 63 | 64 | ## NLP 65 | 66 | - [大规模中文自然语言处理语料](https://github.com/brightmart/nlp_chinese_corpus) 67 | - [用于对话系统的中英文语料](https://github.com/candlewill/Dialog_Corpus) 68 | - [搜狗实验室](http://www.sogou.com/labs/) 69 | - [情感分析︱网络公开的免费文本语料训练数据集汇总](http://blog.csdn.net/sinat_26917383/article/details/51321505) 70 | - [中文情感分析用词语集](http://www.keenage.com/html/c_bulletin_2007.htm) 71 | - [人民日报切分/标注语料库](http://www.icl.pku.edu.cn/icl_res/) 72 | - [哈工大信息检索研究中心(HIT CIR)语言技术平台共享资源](http://ir.hit.edu.cn/demo/ltp/Sharing_Plan.htm) 73 | - [中文句结构树资料库](http://turing.iis.sinica.edu.tw/treesearch/) 74 | - [中文对白语料 chinese conversation corpus](https://github.com/rustch3n/dgk_lost_conv) 75 | - [中文语料小数据：Some useful Chinese corpus datasets](https://github.com/crownpku/Small-Chinese-Corpus) 76 | - [中文人名语料库。中文姓名,姓氏,名字,称呼,日本人名,翻译人名,英文人名](https://github.com/wainshine/Chinese-Names-Corpus) 77 | - [中文突发事件语料库](https://github.com/shijiebei2009/CEC-Corpus) 78 | - [联合国平行语料库](https://conferences.unite.un.org/UNCorpus/zh) 79 | - [保险行业语料库](https://github.com/Samurais/insuranceqa-corpus-zh) 80 | - [中华新华字典数据库。包括歇后语，成语，汉字。提供新华字典API](https://github.com/pwxcoo/chinese-xinhua) 81 | - [用于训练中英文对话系统的语料库 Datasets for Training Chatbot System](https://github.com/candlewill/Dialog_Corpus) 82 | - [最全中华古诗词数据库](https://github.com/chinese-poetry/chinese-poetry) 83 | - [PTT 八卦版問答中文語料](https://github.com/zake7749/Gossiping-Chinese-Corpus) 84 | - [Acemap Knowledge Graph](http://acemap.sjtu.edu.cn/app/AceKG/) :star: 85 | - [A dataset of 200k English plaintext jokes.](https://github.com/taivop/joke-dataset) 86 | - [Alphabetical list of free/public domain datasets with text data for use in NLP](https://github.com/niderhoff/nlp-datasets) 87 | - [A New Multi-Turn, Multi-Domain, Task-Oriented Dialogue Dataset](https://nlp.stanford.edu/blog/a-new-multi-turn-multi-domain-task-oriented-dialogue-dataset/) 88 | - [A text file containing 479k English words for all your dictionary/word-based projects](https://github.com/dwyl/english-words) 89 | - [BBC Sound Effects Archive Resource](http://bbcsfx.acropolis.org.uk/index) 90 | - [CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEB](https://github.com/facebookresearch/LASER/tree/master/tasks/CCMatrix) 91 | - [Chat corpus collection from various open sources](https://github.com/Marsan-Ma/chat_corpus) 92 | - [Chinese Nlp Corpus](https://github.com/SophonPlus/ChineseNlpCorpus) 93 | - [Chinese Text in the Wild](https://ctwdataset.github.io/) 94 | - [CoLA - The Corpus of Linguistic Acceptability](https://nyu-mll.github.io/CoLA/) :star: 95 | - [Collections of Chinese NLP corpus](https://github.com/OYE93/Chinese-NLP-Corpus) 96 | - [Cornell NLVR](http://lic.nlp.cornell.edu/nlvr/) 97 | - [Course materials for Text as Data Lab](https://github.com/leslie-huang/Text-as-Data-Lab-Spr2018) 98 | - [Datasets of Annotated Semantic Relationships](https://github.com/davidsbatista/Annotated-Semantic-Relationships-Datasets) 99 | - [Datasets for Entity Recognition](https://github.com/juand-r/entity-recognition-datasets) 100 | - [Japanese Word Similarity Dataset](https://github.com/tmu-nlp/JapaneseWordSimilarityDataset) 101 | - [Movie Review Data](http://www.cs.cornell.edu/people/pabo/movie-review-data/) 102 | - [Multi-Domain Sentiment Dataset](http://www.cs.jhu.edu/~mdredze/datasets/sentiment/) 103 | - [Open Domain Question Answering](https://ai.google.com/research/NaturalQuestions) :star: 104 | - [Open Speech and Language Resources](http://www.openslr.org/33) :star: 105 | - [Poetry-related datasets collected by THUAIPoet (Jiuge) group.](https://github.com/THUNLP-AIPoet/Datasets) 106 | - [Public Datasets For Recommender Systems](https://github.com/caserec/Datasets-for-Recommneder-Systems) 107 | - [Second International Chinese Word Segmentation Bakeoff Data](http://sighan.cs.uchicago.edu/bakeoff2005/) :star: 108 | - [Taiga Сorpus](https://tatianashavrina.github.io/taiga_site/) 109 | - [Ten thousand books, six million ratings](https://github.com/zygmuntz/goodbooks-10k) 110 | - [The Big Bad NLP Database](https://datasets.quantumstat.com/) 111 | - [The DBpedia Knowledge Base](http://wiki.dbpedia.org/about) 112 | - [The Movies Corpus](https://corpus.byu.edu/movies/) 113 | - [TriviaQA: A Large Scale Dataset for Reading Comprehension and Question Answering](http://nlp.cs.washington.edu/triviaqa/) 114 | - [Yelp Open Dataset](https://www.yelp.com/dataset) 115 | - [70万条对联数据库](https://github.com/wb14123/couplet-dataset) 116 | 117 | ## Video 118 | 119 | - [A large-scale and high-qualityFMA: A Dataset For Music Analysis dataset of annotated musical notes.](https://magenta.tensorflow.org/datasets/nsynth) 120 | - [A large-scale dataset of manually annotated audio events](https://research.google.com/audioset/) :star: 121 | - [FMA: A Dataset For Music Analysis](https://github.com/mdeff/fma) 122 | - [Video Dataset Overview](https://www.di.ens.fr/~miech/datasetviz/) 123 | -------------------------------------------------------------------------------- /courses.md: -------------------------------------------------------------------------------- 1 | # Courses 2 | 3 | * [Berkeley] [Applied Natural Language Processing](http://people.ischool.berkeley.edu/~dbamman/info256.html) 4 | * [Berkeley] [CS294: Deep Reinforcement Learning](http://rll.berkeley.edu/deeprlcourse/) 5 | * [Berkeley] [Stat212b：Topics Course on Deep Learning](http://joanbruna.github.io/stat212b/) 6 | * [CUHK] [ELEG 5040: Advanced Topics in Signal Processing(Introduction to Deep Learning)](https://piazza.com/cuhk.edu.hk/spring2015/eleg5040/home) 7 | * [CMU] [Deep Reinforcement Learning and Control](https://katefvision.github.io/) 8 | * [CMU] [Introduction to Deep Learning](http://deeplearning.cs.cmu.edu/) 9 | * [CMU] [Neural networks for NLP](http://phontron.com/class/nn4nlp2019/) 10 | * [COMS] [W4995 Applied Machine Learning Spring 2018](http://www.cs.columbia.edu/~amueller/comsw4995s18/) 11 | * [David Silver] [RL Course](https://www.youtube.com/watch?v=2pWv7GOvuf0&index=1&list=PL5X3mDkKaJrL42i_jhE4N-p6E2Ol62Ofa) 12 | * [EE 227C][Convex Optimization and Approximation](https://ee227c.github.io/) 13 | * [Emory University] [CS584: Deep Learning](http://nematilab.info/CS584.html) 14 | * [Google] [Udacity Deep Learning Online Course](https://www.youtube.com/watch?v=X_B9NADf2wk&list=PLAwxTw4SYaPn_OWPFT9ulXLuQrImzHfOV&index=2) 15 | * [Hinton] [Neural Networks for Machine Learning](https://www.coursera.org/learn/neural-networks) 16 | * [Hvass Laboratories] [TensorFlow](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ) 17 | * [INTEL] [Deep Learning 501](https://software.intel.com/en-us/ai-academy/students/kits/deep-learning-501) 18 | * [Jeremy Howard] [Deep Learning For Coders](https://www.youtube.com/playlist?list=PLfYUBJiXbdtS2UQRzyrxmyVHoGW0gmLSM) 19 | * [MIT] [Practical Deep LeTensorFlowarning For Coders](http://course.fast.ai/index.html) 20 | * [MIT] [S099: Artificial General Intelligence](https://agi.mit.edu/) 21 | * [MIT] [S094: Deep Learning for Self-Driving Cars](https://selfdrivingcars.mit.edu/) 22 | * [MIT] [S191: Introduction to Deep Learning](http://introtodeeplearning.com/) 23 | * [Nvidia] [Fundamentals of Accelerated Computing with CUDA C/C++](https://courses.nvidia.com/courses/course-v1:DLI+C-AC-01+V1/about) 24 | * [NYU] [Deep Learning by Prof. Yann LeCun](http://cilvr.cs.nyu.edu/doku.php?id=courses:deeplearning2014:start) 25 | * [Oxford] [Deep Learning Course](http://www.computervisiontalks.com/tag/deep-learning-course/) 26 | * [Oxford] [Deep Learning by Prof. Nando de Freitas](https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/) 27 | * [Paris Saclay][Deep Learning course: lecture slides and lab notebooks](https://m2dsupsdlclass.github.io/lectures-labs/) 28 | * [Stanford] [CS231n: Convolutional Neural Networks for Visual Recognition](http://cs231n.stanford.edu/) 29 | * [Stanford] [CS20SI: Tensorflow for Deep Learning Research](https://web.stanford.edu/class/cs20si/) 30 | * [Stanford] [CS224n: Natural Language Processing with Deep Learning](https://web.stanford.edu/class/cs224n/index.html) 31 | * [Stanford] [CS 228: Probabilistic Graphical Models](http://cs.stanford.edu/~ermon/cs228/index.html) 32 | * [Stanford] [CS 20: Tensorflow for Deep Learning Research](https://web.stanford.edu/class/cs20si/) 33 | * [Stanford] [STATS 385: Theories of Deep Learning](https://stats385.github.io/) 34 | * [Toronto] [CSC 2541 Fall 2016:Differentiable Inference and Generative Models](http://www.cs.toronto.edu/~duvenaud/courses/csc2541/index.html) 35 | * [Utah] [Applied Computational Genomics Course at UU](https://github.com/quinlan-lab/applied-computational-genomics) 36 | * [YouTube][OpenCV with Python for Image and Video Analysis](https://www.youtube.com/playlist?list=PLQVvvaa0QuDdttJXlLtAJxJetJcqmqlQq) 37 | * [Python for Data Analysis](https://github.com/cuttlefishh/python-for-data-analysis) 38 | * [Statistical and Discrete Methods for Scientific Computing](http://wpressutexas.net/coursewiki/index.php?title=Main_Page) 39 | * [ANDREW NG][deeplearning.ai](https://www.deeplearning.ai/) 40 | * [吴立德] [《深度学习课程》](http://list.youku.com/albumlist/show?id=21508721&ascending=1&page=1) 41 | * [李宏毅] [Machine Learning and having it Deep and Structured](https://www.youtube.com/playlist?list=PLJV_el3uVTsPMxPbjeX7PicgWbY7F8wW9) 42 | * [林轩田] [机器学习基石](https://www.bilibili.com/video/av12463015/) 43 | * [Introductory Machine Learning Algorithms in Python with scikit-learn](https://egghead.io/courses/introductory-machine-learning-algorithms-in-python-with-scikit-learn) 44 | * [YouTube] [Deep Learning Crash Course (2018)](https://www.youtube.com/playlist?list=PLWKotBjTDoLj3rXBL-nEIPRN9V3a9Cx07) 45 | * [liveVideo Course] [AWS Machine LEarning in Motion (2018)](https://www.manning.com/livevideo/aws-machine-learning-in-motion) 46 | * [liveVideo Course] [Reinforcement Learning in Motion](https://www.manning.com/livevideo/reinforcement-learning-in-motion) 47 | -------------------------------------------------------------------------------- /model_zoo.md: -------------------------------------------------------------------------------- 1 | # Model Zoo 2 | 3 | * 2012 | AlexNet: ImageNet Classification with Deep Convolutional Neural Networks. [`pdf`](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf) [`code`](https://github.com/kratzert/finetune_alexnet_with_tensorflow) 4 | * 2013 | RCNN: Rich feature hierarchies for accurate object detection and semantic segmentation. [`arxiv`](https://arxiv.org/abs/1311.2524) [`code`](https://github.com/rbgirshick/rcnn) 5 | * 2014 | CGNA: Conditional Generative Adversarial Nets. [`arxiv`](https://arxiv.org/abs/1411.1784) [`code`](https://github.com/zhangqianhui/Conditional-Gans) 6 | * 2014 | DeepFaceVariant: Deep Learning Face Representation from Predicting 10,000 Classes. [`pdf`](http://mmlab.ie.cuhk.edu.hk/pdf/YiSun_CVPR14.pdf) [`code`](https://github.com/joyhuang9473/deepid-implementation) 7 | * 2014 | GAN: Generative Adversarial Networks. [`arxiv`](https://arxiv.org/abs/1406.2661) [`code`](https://github.com/goodfeli/adversarial) 8 | * 2014 | GoogLeNet: Going Deeper with Convolutions. [`pdf`](https://www.cs.unc.edu/~wliu/papers/GoogLeNet.pdf) [`code`](https://github.com/google/inception) 9 | * 2014 | OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks. [`arxiv`](https://arxiv.org/abs/1312.6229) [`code`](https://github.com/sermanet/OverFeat) 10 | * 2014 | SPPNet: Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition. [`arxiv`](https://arxiv.org/abs/1406.4729) [`code`](https://github.com/yhenon/keras-spp) 11 | * 2014 | VAE: Semi-Supervised Learning with Deep Generative Models. [`arxiv`](https://arxiv.org/abs/1406.5298) [`code`](https://github.com/dpkingma/nips14-ssl) 12 | * 2014 | VGGNet: Very Deep Convolutional Networks for Large-Scale Image Recognition. [`arxiv`](https://arxiv.org/abs/1409.1556) [`code`](https://gist.github.com/ksimonyan/211839e770f7b538e2d8) 13 | * 2015 | DCGAN: Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. [`arxiv`](https://arxiv.org/abs/1511.06434) [`code`](https://github.com/carpedm20/DCGAN-tensorflow) 14 | * 2015 | DRAW: A Recurrent Neural Network For Image Generation. [`arxiv`](https://arxiv.org/abs/1502.04623) [`code`](https://github.com/ericjang/draw) 15 | * 2015 | Global And Local Attention: Effective Approaches to Attention-based Neural Machine Translation. [`arxiv`](https://arxiv.org/abs/1508.04025) [`code`](https://github.com/giancds/tsf_nmt) 16 | * 2015 | FaceNet: A Unified Embedding for Face Recognition and Clustering. [`arxiv`](https://arxiv.org/abs/1503.03832) [`code`](https://github.com/davidsandberg/facenet) 17 | * 2015 | Fast R-CNN. [`arxiv`](https://arxiv.org/abs/1504.08083) [`code`](https://github.com/rbgirshick/fast-rcnn) 18 | * 2015 | Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. [`arxiv`](https://arxiv.org/abs/1506.01497) [`code`](https://github.com/rbgirshick/py-faster-rcnn) 19 | * 2015 | FCNT: Visual Tracking with Fully Convolutional Networks. [`pdf`](http://www.ee.cuhk.edu.hk/~xgwang/papers/wangOWLiccv15.pdf) [`code`](https://github.com/scott89/FCNT) 20 | * 2015 | Inception V3: Rethinking the Inception Architecture for Computer Vision. [`arxiv`](http://arxiv.org/abs/1512.00567) [`code`](https://github.com/tensorflow/models/tree/master/inception) 21 | * 2015 | LAPGAN: Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks. [`arxiv`](https://arxiv.org/abs/1506.05751) [`code`](https://github.com/facebook/eyescream) 22 | * 2015 | NeuralGPU: Neural GPUs Learn Algorithms. [`arxiv`](http://arxiv.org/abs/1511.08228) [`code`](https://github.com/tensorflow/models/tree/master/neural_gpu) 23 | * 2015 | Pointer Networks. [`arxiv`](https://arxiv.org/abs/1506.03134) [`code`](https://github.com/devsisters/pointer-network-tensorflow) 24 | * 2015 | ResNet: Identity Mappings in Deep Residual Networks. [`arxiv`](https://arxiv.org/pdf/1512.03385v1.pdf) [`arxiv2`](https://arxiv.org/pdf/1605.07146v1.pdf), [`arxiv3`](https://arxiv.org/pdf/1603.05027v2.pdf) [`code`](https://github.com/tensorflow/models/tree/master/resnet) 25 | * 2015 | Skip-Thought Vectors. [`pdf`](https://papers.nips.cc/paper/5950-skip-thought-vectors.pdf) [`code`](https://github.com/tensorflow/models/tree/master/skip_thoughts) 26 | * 2015 | Transformer: Spatial Transformer Networks. [`arxiv`](https://arxiv.org/abs/1506.02025) [`code`](https://github.com/tensorflow/models/tree/master/transformer) 27 | * 2016 | Domain Separation Networks. [`arxiv`](https://arxiv.org/abs/1608.06019) [`code`](https://github.com/tensorflow/models/tree/master/domain_adaptation) 28 | * 2016 | Dp_sgd: Deep Learning with Differential Privacy. [`arxiv`](https://arxiv.org/abs/1607.00133) [`code`](https://github.com/tensorflow/models/tree/master/differential_privacy) 29 | * 2016 | EnergyGAN: Energy-based Generative Adversarial Network. [`arxiv`](https://arxiv.org/abs/1609.03126) [`code`](https://github.com/buriburisuri/ebgan) 30 | * 2016 | Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization. [`arxiv`](https://arxiv.org/abs/1610.02391) [`code`](https://github.com/Ankush96/grad-cam.tensorflow) 31 | * 2016 | Im2txt: Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge. [`arxiv`](http://arxiv.org/abs/1609.06647) [`code`](https://github.com/tensorflow/models/tree/master/im2txt) 32 | * 2016 | InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets. [`arxiv`](https://arxiv.org/abs/1606.03657) [`code`](https://github.com/buriburisuri/supervised_infogan) 33 | * 2016 | Multiple_teachers: Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data. [`arxiv`](https://arxiv.org/abs/1610.05755) [`code`](https://github.com/tensorflow/models/tree/master/differential_privacy) 34 | * 2016 | Neural Programmer: Learning a Natural Language Interface with Neural Programmer. [`arxiv`](https://arxiv.org/abs/1611.08945) [`code`](https://github.com/tensorflow/models/tree/master/neural_programmer) 35 | * 2016 | PixelCNN: Conditional Image Generation with PixelCNN Decoders. [`arxiv`](https://arxiv.org/abs/1606.05328) [`code`](https://github.com/kundan2510/pixelCNN) 36 | * 2016 | Pix2Pix: Image-to-Image Translation with Conditional Adversarial Networks. [`arxiv`](https://arxiv.org/abs/1611.07004) [`code`](https://github.com/yenchenlin/pix2pix-tensorflow) 37 | * 2016 | PVANet: Lightweight Deep Neural Networks for Real-time Object Detection. [`arxiv`](https://arxiv.org/abs/1611.08588) [`code`](https://github.com/sanghoon/pva-faster-rcnn) 38 | * 2016 | R-FCN: Object Detection via Region-based Fully Convolutional Networks. [`arxiv`](https://arxiv.org/abs/1605.06409) [`code`](https://github.com/Orpine/py-R-FCN) 39 | * 2016 | SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient. [`arxiv`](https://arxiv.org/abs/1609.05473) [`code`](https://github.com/LantaoYu/SeqGAN) 40 | * 2016 | SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size. [`arxiv`](https://arxiv.org/abs/1602.07360) [`code`](https://github.com/songhan/SqueezeNet-Deep-Compression) 41 | * 2016 | StreetView Tensorflow Recurrent End-to-End Transcription (STREET) Model. [`arxiv`](https://arxiv.org/abs/1702.03970) [`code`](https://github.com/tensorflow/models/tree/master/street) 42 | * 2016 | Swivel: Improving Embeddings by Noticing What's Missing. [`arxiv`](https://arxiv.org/abs/1602.02215) [`code`](https://github.com/tensorflow/models/tree/master/swivel) 43 | * 2016 | SyntaxNet: Globally Normalized Transition-Based Neural Networks. [`arxiv`](https://arxiv.org/abs/1603.06042) [`code`](https://github.com/tensorflow/models/tree/master/syntaxnet) 44 | * 2016 | Textsum: Sequence-to-Sequence with Attention Model for Text Summarization. [`pdf`](https://openreview.net/pdf?id=gZ9OMgQWoIAPowrRUAN6) [`code`](https://github.com/tensorflow/models/tree/master/textsum) 45 | * 2016 | VGNA: Generative Adversarial Networks as Variational Training of Energy Based Models. [`arxiv`](https://arxiv.org/abs/1611.01799) [`code`](https://github.com/Shuangfei/vgan) 46 | * 2017 | Learning to Remember Rare Events. [`pdf`](https://openreview.net/pdf?id=SJTQLdqlg) [`code`](https://github.com/tensorflow/models/tree/master/learning_to_remember_rare_events) 47 | * 2017 | LFADS - Latent Factor Analysis via Dynamical Systems. [`arxiv`](https://arxiv.org/abs/1608.06315) [`code`](https://github.com/tensorflow/models/tree/master/lfads) 48 | * 2017 | SalGAN: Visual Saliency Prediction with Generative Adversarial Networks. [`arxiv`](https://arxiv.org/abs/1701.01081) [`code`](https://github.com/imatge-upc/saliency-salgan-2017) 49 | * 2017 | WGAN: Wasserstein GAN. [`arxiv`](https://arxiv.org/abs/1701.07875) [`code`](https://github.com/Zardinality/WGAN-tensorflow) 50 | * 2017 | BEGAN: Boundary Equilibrium Generative Adversarial Networks. [`arxiv`](https://arxiv.org/abs/1703.10717) [`code`](https://github.com/carpedm20/BEGAN-tensorflow) 51 | -------------------------------------------------------------------------------- /papers/2010.md: -------------------------------------------------------------------------------- 1 | ## 2010 2 | 3 | ### Deep Learning 4 | 5 | - A Connection Between Score Matching and Denoising Autoencoders. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjCqMCn8dLQAhWETLwKHRCWCY4QFgggMAA&url=http%3A%2F%2Fwww.iro.umontreal.ca%2F~vincentp%2FPublications%2Fsmdae_techreport.pdf&usg=AFQjCNFwiC2GowBsAbO9In3uQZZVMUndJA)] 6 | - Attribute-Based Transfer Learning for Object Categorization with Zero/One Training Example. [[url](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjkl_XOuY7RAhUM0mMKHc6DBaIQFggfMAA&url=http%3A%2F%2Fwww.jdl.ac.cn%2Fproject%2FfaceId%2Fpaperreading%2FPaper%2Fmnkan_20101217.pdf&usg=AFQjCNEatmtDtSjNqvGTTL2evxt5EmhbbQ)] 7 | - A Practical Guide to Training Restricted Boltzmann Machines. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwj5u-DS8NLQAhUI2LwKHaAOATgQFgghMAA&url=https%3A%2F%2Fwww.cs.toronto.edu%2F~hinton%2Fabsps%2FguideTR.pdf&usg=AFQjCNEgnNMhuCJpMLeg16W18j3_YLicDA)] :star: 8 | - A Theoretical Analysis of Feature Pooling in Visual Recognition. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwi2loa_8NLQAhUCu7wKHSIbDOEQFggjMAA&url=http%3A%2F%2Fece.duke.edu%2F~lcarin%2FBo12.3.2010.ppt&usg=AFQjCNHpwoX8U2vti9SGf1KNxNi9Vv_MSw)] 9 | - Convolutional Networks and Applications in Vision. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjiwI-u8NLQAhUMfbwKHcXiCEoQFggiMAA&url=http%3A%2F%2Fkoray.kavukcuoglu.org%2Fpublis%2Flecun-iscas-10.pdf&usg=AFQjCNGU_3TXNc1D4WIe99Y4KTLSSMQfQg)] 10 | - [DeepBigSimpleNet] Deep Big Simple Neural Nets Excel on Handwritten Digit Recognition. [`arxiv`](https://arxiv.org/pdf/1003.0358v1.pdf) :star: 11 | - Deep learning via Hessian-free optimization. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&uact=8&ved=0ahUKEwi9wsSY8NLQAhWHU7wKHaVPA98QFggrMAE&url=http%3A%2F%2Fwww.cs.toronto.edu%2F~jmartens%2Fdocs%2FHF_talk.pdf&usg=AFQjCNGhJp0P3WRP_KLXVGbzDejWIiVBDQ)] 12 | - Efficient Learning of Deep Boltzmann Machines. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjwwJ2D8NLQAhVFe7wKHeDGC4gQFggiMAA&url=http%3A%2F%2Fwww.jmlr.org%2Fproceedings%2Fpapers%2Fv9%2Fsalakhutdinov10a%2Fsalakhutdinov10a.pdf&usg=AFQjCNHQFRkt3xL3Df2xYAhnzkuDwBZF5A)] 13 | - Image Classification using Super-Vector Coding of Local Image Descriptors. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjhsvfq79LQAhXCU7wKHV2sAlAQFgggMAA&url=http%3A%2F%2Fcs.utsa.edu%2F~qitian%2Fseminar%2FSpring11%2F02_18_11%2FECCV10.pdf&usg=AFQjCNHU9qpi-5CGTWqER_WjwBfbDfYoSg)] 14 | - Learning Convolutional Feature Hierarchies for Visual Recognition. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwiIq_fS79LQAhVIa7wKHbYbCCUQFgggMAA&url=http%3A%2F%2Fcs.nyu.edu%2F~ylan%2Ffiles%2Fpubli%2Fkoray-nips-10.pdf&usg=AFQjCNEvxZlUog_aksNxU7IXaT60GTdmYw)] 15 | - Learning Deep Architectures for AI. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&uact=8&ved=0ahUKEwiblfi979LQAhUDNrwKHVPwDW4QFggtMAE&url=http%3A%2F%2Fwww.iro.umontreal.ca%2F~bengioy%2Fpapers%2Fftml_book.pdf&usg=AFQjCNHEnyYAwBXG425OaKQRV5TFAieGKg)] :star: 16 | - Learning Mid-Level Features For Recognition. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjJ8fWv79LQAhUJe7wKHWEMCD8QFggiMAA&url=http%3A%2F%2Fece.duke.edu%2F~lcarin%2Fboureau-cvpr-10.pdf&usg=AFQjCNG58A71VMxjm8AayNjFS4lSdvCAHQ)] :star: 17 | - Learning Restricted Boltzmann Machines using Mode-Hopping MCMC. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjLpK_p7tLQAhULS7wKHUrKC64QFgghMAA&url=http%3A%2F%2Fwww.icmlc.org%2Ficmlc2012%2F021_icmlc2012.pdf&usg=AFQjCNFR_zwSanoa8_8C8QeROvzBpyDp7g)] :star: 18 | - Locality-constrained Linear Coding for Image Classification.[[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwiJ3_PY7tLQAhXLu7wKHcLNBu0QFgggMAA&url=http%3A%2F%2Fwww.ifp.illinois.edu%2F~jyang29%2Fpapers%2FCVPR10-LLC.pdf&usg=AFQjCNGuv8ZCmW0Vezde_GWY0_99lwXedA)] :star: 19 | - Modeling Pixel Means and Covariances Using Factorized Third-Order Boltzmann Machines. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjY_a_E7tLQAhVHUbwKHVakDkgQFggiMAA&url=http%3A%2F%2Fwww.cs.toronto.edu%2F~fritz%2Fabsps%2Franzato_cvpr2010.pdf&usg=AFQjCNEvRzUohuLf20MCEueih8TuBeCVGg)] 20 | - On the Convergence Properties of Contrastive Divergence. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwiru9mq7tLQAhVBwLwKHQm9DmIQFggiMAA&url=http%3A%2F%2Fwww.jmlr.org%2Fproceedings%2Fpapers%2Fv9%2Fsutskever10a%2Fsutskever10a.pdf&usg=AFQjCNFWhei-DQdPqRIw7LXpkobqewIBEg)] 21 | - Regularized estimation of image statistics by Score Matching. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjBv7CY7tLQAhWCWLwKHWLrCyYQFgggMAA&url=http%3A%2F%2Fyann.lecun.com%2Fexdb%2Fpublis%2Fpdf%2Fkingma-nips-10.pdf&usg=AFQjCNE3vLq80xVTEpq7YC_UVC6Od-Gt-A)] 22 | - Stacked Denoising Autoencoders Learning Useful Representations in a Deep Network with a Local Denoising Criterion. [[remote url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwiIkseH7tLQAhWLyLwKHa5AAO8QFgggMAA&url=http%3A%2F%2Fwww.jmlr.org%2Fpapers%2Fvolume11%2Fvincent10a%2Fvincent10a.pdf&usg=AFQjCNEMltn5KkI7GimzmqjdcADacv7RMw)] :star: 23 | - Why Does Unsupervised Pre-training Help Deep Learning?. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwiG_7X27dLQAhXGe7wKHZqrC6oQFggdMAA&url=http%3A%2F%2Fjmlr.org%2Fpapers%2Fvolume11%2Ferhan10a%2Ferhan10a.pdf&usg=AFQjCNGCudAMh069uKLudMcU6QNfrjS-GA)] :star: 24 | - Hierarchical Reinforcement Learning for Adaptive Text Generation. [[pdf](https://www.google.com.hk/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjC6cD2rpTRAhXGslQKHSiaApcQFggcMAA&url=%68%74%74%70%3a%2f%2f%77%77%77%2e%61%63%6c%77%65%62%2e%6f%72%67%2f%61%6e%74%68%6f%6c%6f%67%79%2f%57%31%30%2d%34%32%30%34&usg=AFQjCNFfz_ZhikX_Zypf_Omm2LKANDKagQ)] 25 | 26 | ### Transfer learning 27 | 28 | - Boosting for transfer learning with multiple sources. [[pdf](http://www.vision.ucla.edu/~doretto/publications/yaoD10cvpr.pdf)] 29 | - Bregman Divergence-Based Regularization for Transfer Subspace Learning. [[pdf](http://www.cs.utexas.edu/~ssi/TKDE2010.pdf)] 30 | - Cross-Domain Sentiment Classification via Spectral Feature Alignment. [[pdf](http://wenku.baidu.com/link?url=uW-mqO2w3ZkRjCv2YLedkn5-9H-D5lthBvCaXmqV4VFboXGbWesL_tR9i3bnvzTTlRe5ZV10MpTWVzcRLuJ0k3-K2AaWAx8s4BP2g4HWfTy)] :star: 31 | - Safety in numbers: Learning categories from few examples with multi model knowledge transfer. [[pdf](http://publications.idiap.ch/downloads/papers/2011/Tommasi_CVPR2010_2010.pdf)] :star: 32 | - Transfer Learning in Collaborative Filtering for Sparsity Reduction.[[url](http://www.aaai.org/ocs/index.php/AAAI/AAAI10/paper/viewFile/1649/1963/)] 33 | - Transfer Learning for Collective Link Prediction in Multiple Heterogenous Domains. [[url](http://machinelearning.wustl.edu/mlpapers/paper_files/icml2010_CaoLY10.pdf)] 34 | - Transfer Learning on Heterogenous Feature Spaces via Spectral Transformation. [[pdf](https://www.cs.uic.edu/~xiaoxiao/paper/xiaoxiaoICDM10_2.pdf)] -------------------------------------------------------------------------------- /papers/2011.md: -------------------------------------------------------------------------------- 1 | ## 2011 2 | 3 | ### Deep Learning 4 | 5 | - Adaptive Deconvolutional Networks for Mid and High Level Feature Learning. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwiy8IjP9NLQAhVKwLwKHbQdADsQFgggMAA&url=https%3A%2F%2Fwww.cs.nyu.edu%2F~gwtaylor%2Fpublications%2Fzeilertaylorfergus_iccv2011.pdf&usg=AFQjCNEwqEab7zJTWSbuqUhWQiCkjmjcZw)] 6 | - Asymptotic efficiency of deterministic estimators for discrete energy-based models Ratio matching and pseudolikelihood. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwiOj5O79NLQAhUFTrwKHXrfBrEQFggjMAA&url=http%3A%2F%2Farxiv.org%2Fabs%2F1202.3746&usg=AFQjCNFm24ljb1Av16DG_svha7RMFxnU0w)] 7 | - Building high-level features using large scale unsupervised learning. [`arxiv`](https://arxiv.org/abs/1112.6209) 8 | - Contractive Auto-Encoders-Explicit Invariance During Feature Extraction. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwiBzu6q9NLQAhVHwbwKHc7iAhUQFgggMAA&url=http%3A%2F%2Fwww.icml-2011.org%2Fpapers%2F455_icmlpaper.pdf&usg=AFQjCNGgSPEnpKGGUf4OmrpaOCJR-qfHaQ)] 9 | - Cross-domain activity recognition via transfer learning. [[url](http://dl.acm.org/citation.cfm?id=1998725)] 10 | - Heterogeneous Transfer Learning for Image Classification. [[url](https://www.researchgate.net/profile/Zhongqi_Lu/publication/221605039_Heterogeneous_Transfer_Learning_for_Image_Classification/links/0046352ca689c9f097000000.pdf)] :star: 11 | - Kinship Verification through Transfer Learning. [[url](http://ijcai.org/Proceedings/11/Papers/422.pdf)] 12 | - Learning Deep Energy Models. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjCu8qX9NLQAhWEULwKHWpiDqwQFggiMAA&url=http%3A%2F%2Fai.stanford.edu%2F~ang%2Fpapers%2Ficml11-DeepEnergyModels.pdf&usg=AFQjCNHwzaupJiOstpM5KddMyBcWUCWBfg)] 13 | - Learning image representations from the pixel level via hierarchical sparse coding. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwiJs6qF9NLQAhUIhbwKHcvgAysQFgggMAA&url=http%3A%2F%2Fwww.linyq.com%2F1926.pdf&usg=AFQjCNFoKzZr8tf-zWm4RQH6M3vXOGG4OA)] 14 | - Natural Language Processing (almost) from Scratch. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjJwf3089LQAhXFWbwKHcuJC5oQFggiMAA&url=https%3A%2F%2Farxiv.org%2Fabs%2F1103.0398&usg=AFQjCNE-v7uyQIX9MmRXHoPw17xfnwGjJw)] :star: 15 | - On Autoencoders and Score Matching for Energy Based Models. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjA2KLf89LQAhVLv7wKHdoQCp8QFgggMAA&url=https%3A%2F%2Fpdfs.semanticscholar.org%2F9b0d%2F728939de7810fa442fb706ea1dc3b0d26930.pdf&usg=AFQjCNFcEM5CTiDxRY4P8hgnzuU8jLG2Cg)] 16 | - On Optimization Methods for Deep Learning. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjGt4m889LQAhWBgLwKHTCsDnoQFggiMAA&url=http%3A%2F%2Fai.stanford.edu%2F~ang%2Fpapers%2Ficml11-OptimizationForDeepLearning.pdf&usg=AFQjCNG8DmFVkQZ_zg0J62bG-rWPLUbXvQ)] 17 | - On the Expressive Power of Deep Architectures. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwiYzeDR89LQAhVLebwKHY9SC28QFggiMAA&url=https%3A%2F%2Fpdfs.semanticscholar.org%2F75b3%2F2007ae3c5dc2a4009503a3a9d6fc9614f9e7.pdf&usg=AFQjCNEMa7EyjQdRIzyP1MAMbYgPI36QEg)] 18 | - Spike-and-Slab Sparse Coding for Unsupervised Feature Discovery. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&uact=8&ved=0ahUKEwjK7qqa89LQAhXLTLwKHXPjBIwQFggpMAE&url=https%3A%2F%2Farxiv.org%2Fpdf%2F1201.3382&usg=AFQjCNEgFUQgcIZqlcsOnuFAJwCiid4_QQ)] 19 | - Unsupervised Learning of Hierarchical Representations with Convolutional Deep Belief Networks. [[url]](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjW7a7p8tLQAhVJ2LwKHdxYBsgQFggiMAA&url=https%3A%2F%2Fwww.cs.toronto.edu%2F~rgrosse%2Fcacm2011-cdbn.pdf&usg=AFQjCNGPGi2zTFtKM5Ang-I5BfTL5MEn6Q)] 20 | - Unsupervised Models of Images by Spike-and-Slab RBMs. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjympXE8tLQAhXJS7wKHRO4CgEQFggdMAA&url=http%3A%2F%2Fwww.icml-2011.org%2Fpapers%2F591_icmlpaper.pdf&usg=AFQjCNEXIYAbEqynz3ztjTuhvCgiTUaY3A)] 21 | 22 | 23 | ### Attention 24 | 25 | - Learning where to Attend with Deep Architectures for Image Tracking. [`arxiv`](https://arxiv.org/abs/1109.3737) 26 | 27 | ### Transfer learning 28 | 29 | - Domain Adaptation for Large-Scale Sentiment Classification - A Deep Learning Approach. [[pdf](http://www.docin.com/p-325419659.html)] :star: 30 | - Heterogeneous domain adaptation using manifold alignment. [[pdf](http://fodava.gatech.edu/sites/default/files/FODAVA-11-38.pdf)] 31 | - Heterogeneous Transfer Learning for Image Classification. [[pdf](http://www.cse.ust.hk/~yinz/HTL_AAAI11.pdf)] [[code](http://www.cse.ust.hk/~yinz/htl4ic.zip)] 32 | - Learning from multiple outlooks. [[pdf](http://people.ee.duke.edu/~lcarin/275_icmlpaper.pdf)] 33 | - Multi-source domain adaptation and its application to early detection of fatigue. [[pdf](http://web.cs.ucdavis.edu/~davidson/Publications/op0466-yeAemb1.pdf)] :star: 34 | - What you saw is not what you get: Domain adaptation using asymmetric kernel transforms. [[pdf](http://web.cse.ohio-state.edu/~kulis/pubs/cvpr_adapt.pdf)] -------------------------------------------------------------------------------- /papers/2012.md: -------------------------------------------------------------------------------- 1 | ## 2012 2 | 3 | ### Deep Learning 4 | 5 | - A Better Way to Pretrain Deep Boltzmann Machines. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjj5pzG-dLQAhUFTbwKHYtVCMgQFgggMAA&url=https%3A%2F%2Fpapers.nips.cc%2Fpaper%2F4610-a-better-way-to-pretrain-deep-boltzmann-machines.pdf&usg=AFQjCNHwpqy-iepwXiEs6dCOKKBHAuSYsw)] 6 | - A GeneratDeep neural networks for acoustic modeling in speech recognition: The shared views of four research groupsive Process for Sampling Contractive Auto-Encoders.[[pdf](docs/2012/A Generative Process for Sampling Contractive Auto-Encoders.pdf)] [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwiV-Ya2-dLQAhXDw7wKHU2qCH0QFgggMAA&url=http%3A%2F%2Ficml.cc%2F2012%2Fpapers%2F910.pdf&usg=AFQjCNFjnp-StAS0Cs-v0xdmFa45PbKYiA)] 7 | - An Efficient Learning Procedure for Deep Boltzmann Machines. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=3&cad=rja&uact=8&ved=0ahUKEwjbydSl-dLQAhXDiLwKHcIQAT4QFggyMAI&url=https%3A%2F%2Fai2-s2-pdfs.s3.amazonaws.com%2F0e7c%2F7f233ff98c45a215df32891ea0f51419d15a.pdf&usg=AFQjCNHKuBWS_aql6gpCA_-lLqFGwdgjlQ)] 8 | - Autoencoders, Unsupervised Learning, and Deep Architectures. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwius5eU-dLQAhXMvLwKHbbNAVIQFggiMAA&url=http%3A%2F%2Fwww.jmlr.org%2Fproceedings%2Fpapers%2Fv27%2Fbaldi12a%2Fbaldi12a.pdf&usg=AFQjCNFg1S7JSu5qv73NboNb3zNABV306g)] 9 | - Building High-level Features Using Large Scale Unsupervised Learning. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwidzcmE-dLQAhUDyrwKHVmeBvwQFgggMAA&url=http%3A%2F%2Fresearch.google.com%2Farchive%2Funsupervised_icml2012.pdf&usg=AFQjCNE1E3ZWaP8H7fyFNFEETUavcCHDpQ)] 10 | - Deep Learning of Representations for Unsupervised and Transfer Learning. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwj9wY_y-NLQAhWMvrwKHXHHAnMQFggdMAA&url=http%3A%2F%2Fwww.jmlr.org%2Fproceedings%2Fpapers%2Fv27%2Fbengio12a%2Fbengio12a.pdf&usg=AFQjCNHYxXXM68fhs49Bx1jaR3gKvfU6kA)] 11 | - Deep Learning via Semi-Supervised Embedding. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwiVmNPf-NLQAhVIVbwKHTXXD-4QFgggMAA&url=http%3A%2F%2Fwww.thespermwhale.com%2Fjaseweston%2Fpapers%2Fdeep_embed.pdf&usg=AFQjCNG1B5M_aZ6n0NJ5mgY3StZ4lLAI5g)] 12 | - Deep Learning with Hierarchical Convolutional Factor Analysis. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjp7N3O-NLQAhVJzbwKHWZIC64QFgggMAA&url=http%3A%2F%2Fece.duke.edu%2F~lcarin%2FBDL15.pdf&usg=AFQjCNGmSpjNt3jBO1DUbG02INWnYip8qA)] 13 | - Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups.[[url](https://static.googleusercontent.com/media/research.google.com/zh-CN//pubs/archive/38131.pdf)] :star: 14 | - RDiscriminative Learning of Sum-Product Networks. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwinvo2--NLQAhXBVLwKHcGQB6cQFggiMAA&url=http%3A%2F%2Fhomes.cs.washington.edu%2F~pedrod%2Fpapers%2Fnips12.pdf&usg=AFQjCNFoxpHvaTqgybv0T4F_TnNzYRwSOw)] 15 | - [AlexNet] [ImageNet Classification with Deep Convolutional Neural Networks](http://www.gageet.com/2014/09140.php). [[pdf](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf)] [[code](https://code.google.com/p/cuda-convnet/)] [[tensorflow](https://github.com/kratzert/finetune_alexnet_with_tensorflow)]:star: 16 | - [Dropout] Improving neural networks by preventing co-adaptation of feature detectors. [`arxiv`](https://arxiv.org/abs/1207.0580) :star: 17 | - Invariant Scattering Convolution Networks. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&uact=8&ved=0ahUKEwjN7O-l9tLQAhWDfLwKHcSHCPgQFgguMAE&url=https%3A%2F%2Farxiv.org%2Fpdf%2F1203.1513&usg=AFQjCNFh7CFKJ4K2l03GJ7j4rV3FFhhR7A)] 18 | - Learning with Hierarchical-Deep Models. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&uact=8&ved=0ahUKEwiQnd6V9tLQAhULwrwKHRkMCkcQFggpMAE&url=http%3A%2F%2Fwww.utstat.toronto.edu%2F~rsalakhu%2Fpapers%2Fpami_submit.pdf&usg=AFQjCNEleabAyzRhoOO2L3hJSUVCIMR0uA)] 19 | - Practical Bayesian Optimization of Machine Learning Algorithms. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjR6b2H9tLQAhUMOrwKHdK9BewQFgggMAA&url=https%3A%2F%2Fpapers.nips.cc%2Fpaper%2F4522-practical-bayesian-optimization-of-machine-learning-algorithms.pdf&usg=AFQjCNG6lWpnHjwLD2PWPxlhxxaz02op2g)] :star: 20 | - Practical Recommendations for Gradient-Based Training of Deep Architectures. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjh-u_w9dLQAhUENbwKHSxSATAQFgggMAA&url=https%3A%2F%2Farxiv.org%2Fabs%2F1206.5533&usg=AFQjCNFGWiOvwFGFqB9l5X_pGanaGWiNgg)] 21 | - Random Search for Hyper-Parameter Optimization. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwiYuPXQ9dLQAhUPOrwKHdNFBUAQFggiMAA&url=http%3A%2F%2Fwww.jmlr.org%2Fpapers%2Fvolume13%2Fbergstra12a%2Fbergstra12a.pdf&usg=AFQjCNFKAQbDd5l0Q7WH36ejee4ahKlZQg)] :star: 22 | 23 | ### Transfer learning 24 | 25 | - Cross-domain co-extraction of sentiment and topic lexicons. [[pdf](http://anthology.aclweb.org/P/P12/P12-1043.pdf)] :star: 26 | - Domain adaptation from multiple sources: a domain-dependent regularization approach. [[pdf](http://vc.sce.ntu.edu.sg/tl_papers/TNN-DAM.pdf)] 27 | - Domain Transfer Multiple Kernel Learning. [[pdf](http://blog.sciencenet.cn/home.php?mod=attachment&id=14109)] 28 | - Information-Theoretical Learning of Discriminative Clusters for Unsupervised Domain Adaptation. [[pdf](http://people.ee.duke.edu/~lcarin/Liming11.16.2012.pdf)] 29 | - Learning with Augmented Features for Heterogeneous Domain Adaptation. [[pdf](http://www.lxduan.info/papers/DuanICML2012.pdf)] 30 | - Semi-Supervised Kernel Matching for Domain Adaptation. [[pdf](https://cis.temple.edu/~yuhong/research/papers/aaai12a.pdf)] 31 | - Supplementary Material Geodesic Flow Kernel for Unsupervised Domain Adaptation. [[pdf](http://crcv-web.eecs.ucf.edu/people/faculty/Gong/Paper/GFK_cvpr12_supp.pdf)] 32 | - TALMUD: transfer learning for multiple domains. [[pdf](http://www.ise.bgu.ac.il/faculty/liorr/moreno2012talmud.pdf)] -------------------------------------------------------------------------------- /papers/2013.md: -------------------------------------------------------------------------------- 1 | # 2013 2 | 3 | ### Deep Learning 4 | 5 | - Adaptive dropout for training deep neural networks. [[url](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjDrbLe0NDQAhULrFQKHXfIBOcQFgggMAA&url=https%3A%2F%2Fpapers.nips.cc%2Fpaper%2F5032-adaptive-dropout-for-training-deep-neural-networks.pdf&usg=AFQjCNEIvLpW_VwelyHjOxQ7up1Wc4djkA)] 6 | - [VAE] [Auto-Encoding Variational Bayes.](http://zhouchang.info/blog/2016-04-11/VAE.html) [`arxiv`](https://arxiv.org/abs/1312.6114) [`url`](http://dpkingma.com/wordpress/wp-content/uploads/2014/05/2014-03_talk_iclr.pdf) :star: 7 | - Better Mixing via Deep Representations. [url](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&uact=8&ved=0ahUKEwj629740dDQAhUmi1QKHRjaB4AQFgguMAE&url=https%3A%2F%2Farxiv.org%2Fpdf%2F1207.4404&usg=AFQjCNEVC3VegGSWQXm-XJvWXOjjlk1VIA) 8 | - Deep Fisher Networks for Large-Scale Image Classification. [[url](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwifnNeg0tDQAhWpsVQKHU3wB4sQFgggMAA&url=https%3A%2F%2Fwww.robots.ox.ac.uk%2F~vgg%2Fpublications%2F2013%2FSimonyan13b%2Fsimonyan13b.pdf&usg=AFQjCNEixGKMTXtLPadNICunAuLHvv-iog)] 9 | - Deep Learning of Representations-looking forward. [[url](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&uact=8&ved=0ahUKEwiVocvG0tDQAhWLhVQKHRcvAIcQFggpMAE&url=https%3A%2F%2Farxiv.org%2Fpdf%2F1305.0445&usg=AFQjCNFmv3e_Pd6z34Er4V3zvUqO77QXog)] 10 | - Deep Neural Networks for Object Detection. [[url](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwie1brk0tDQAhWhxlQKHVSQACIQFggiMAA&url=https%3A%2F%2Fpdfs.semanticscholar.org%2F713f%2F73ce5c3013d9fb796c21b981dc6629af0bd5.pdf&usg=AFQjCNF79vro3uwWMO53Sqh9Imh62uCl_A)] :star: 11 | - Dropout Training as Adaptive Regularization. [[url](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&uact=8&ved=0ahUKEwjmzZCU09DQAhXpz1QKHYuNC1AQFgguMAE&url=http%3A%2F%2Fpapers.nips.cc%2Fpaper%2F4882-dropout-training-as-adaptive-regularization.pdf&usg=AFQjCNEChKFYrerSVjmcTeUW8JdtiLisGw)] 12 | - Efficient Estimation of Word Representations in Vector Space. [[url](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjYx_TI09DQAhXihFQKHcNpBeQQFgggMAA&url=https%3A%2F%2Farxiv.org%2Fpdf%2F1301.3781&usg=AFQjCNFo6E4qrQLPJrMm4O4UzOEivh0Crw)] :star: 13 | - Exploiting Similarities among Languages for Machine Translation. [[url](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&uact=8&ved=0ahUKEwj245_009DQAhWEjFQKHSLHDTkQFggsMAE&url=http%3A%2F%2Fresearch.google.com%2Fpubs%2Farchive%2F44931.pdf&usg=AFQjCNEIiCd3WIURIXZY0fch73RrpgnhvQ)] 14 | - Generalized Denoising Auto-Encoders as Generative Models. [`url`](http://papers.nips.cc/paper/5023-generalized-denoising-auto-encoders-as-generative-models) [`code`](https://github.com/yaoli/GSN) 15 | - Generating Sequences With Recurrent Neural Networks. [`arxiv`](https://arxiv.org/abs/1308.0850) :star: 16 | - Generative Stochastic Networks Trainable by Backprop. [`arxiv`](https://arxiv.org/abs/1306.1091) [`code`](https://github.com/yaoli/GSN) 17 | - Learning a Deep Compact Image Representation for Visual Tracking. [[url](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&uact=8&ved=0ahUKEwi3zYW01tDQAhWIwFQKHUmCCnMQFggqMAE&url=http%3A%2F%2Fwinsty.net%2Fpapers%2Fdlt.pdf&usg=AFQjCNFUQTWp6bQNt1AhJ50sXR7Gj-azAA)] 18 | - Learning Hierarchical Features for Scene Labeling. [[url](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjip9nP1tDQAhWs0FQKHVE3CiUQFggiMAA&url=http%3A%2F%2Fyann.lecun.com%2Fexdb%2Fpublis%2Fpdf%2Ffarabet-pami-13.pdf&usg=AFQjCNGaHp1t2JKhTOWjhqAzmWrcMbnQ-A)] :star: 19 | - Learning Multi-level Sparse Representations. [[url](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjG5_uX19DQAhVCrlQKHSiqB64QFggiMAA&url=http%3A%2F%2Fpapers.nips.cc%2Fpaper%2F5076-learning-multi-level-sparse-representations.pdf&usg=AFQjCNGnJjQja7ddxoLTaC6Zr_VR4tqKaw)] 20 | - [Maxout] Maxout Networks. [[url](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&uact=8&ved=0ahUKEwj5vpjM19DQAhUos1QKHZ6zAQcQFggnMAE&url=https%3A%2F%2Farxiv.org%2Fpdf%2F1302.4389&usg=AFQjCNHCC8d-MyLhvu0kw2dsmCZHa17OXA)] :star: 21 | - No More Pesky Learning Rates. [[url](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjby9Lu19DQAhXCilQKHVuJA0MQFggdMAA&url=https%3A%2F%2Farxiv.org%2Fpdf%2F1206.1106&usg=AFQjCNFM0JupBcHvTGZck8kaO4xEvcYXdg)] 22 | - On autoencoder scoring. [[url](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjAnuio2NDQAhWHh1QKHf3PAXIQFggdMAA&url=http%3A%2F%2Fwww.jmlr.org%2Fproceedings%2Fpapers%2Fv28%2Fkamyshanska13.pdf&usg=AFQjCNGNvTTTRcMXCdGjbhaekXcIqEMjOw)] 23 | - On the difficulty of training recurrent neural networks. [[url](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwj3z4LN2NDQAhXjjlQKHSPhCaAQFgggMAA&url=http%3A%2F%2Fwww.jmlr.org%2Fproceedings%2Fpapers%2Fv28%2Fpascanu13.pdf&usg=AFQjCNEATfq_Z8jFkNJ_zO566QBDMyyaSw)] 24 | - On the importance of initialization and momentum in deep learning. [[url](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwifhdjp2NDQAhXCq1QKHarVB2cQFggdMAA&url=http%3A%2F%2Fwww.cs.toronto.edu%2F~hinton%2Fabsps%2Fmomentum.pdf&usg=AFQjCNF_On1gl-3iNj7fJ6EYSrP1RBckPg)] 25 | - Provable Bounds for Learning Some Deep Representations. [`arxiv`](https://arxiv.org/abs/1310.6343) :star: 26 | - Regularization of Neural Networks using DropConnect. [[url](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjL_5GH2dDQAhVhq1QKHVzuBzIQFggiMAA&url=http%3A%2F%2Fwww.matthewzeiler.com%2Fpubs%2Ficml2013%2Ficml2013.pdf&usg=AFQjCNFy6eyEr7XS251AkptfvV537UPQAA)] 27 | - Representation Learning A Review and New Perspectives. [[url](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwj9zK6s2tDQAhWjjVQKHXq1A5oQFggiMAA&url=http%3A%2F%2Fwww.cl.uni-heidelberg.de%2Fcourses%2Fws14%2Fdeepl%2FBengioETAL12.pdf&usg=AFQjCNEQI9Rst49wWNFRe1TG9nZscxS2QQ)] :star: 28 | - [RCNN] [Rich feature hierarchies for accurate object detection and semantic segmentation.](http://lib.csdn.net/article/deeplearning/46183?knId=1734) [`arxiv`](https://arxiv.org/abs/1311.2524) [`code`](https://github.com/rbgirshick/rcnn) :star: 29 | - Scaling up Spike-and-Slab Models for Unsupervised Feature Learning. [[url](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&uact=8&ved=0ahUKEwjb2dzP2tDQAhXKyVQKHSxGCB8QFggqMAE&url=https%3A%2F%2Farxiv.org%2Fpdf%2F1201.3382&usg=AFQjCNEgFUQgcIZqlcsOnuFAJwCiid4_QQ)] 30 | - Speech Recognition with Deep Recurrent Neural Networks. [`arxiv`](https://arxiv.org/abs/1303.5778) :star: 31 | - Stochastic Pooling for Regularization of Deep Convolutional Neural Networks. [[url](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwiB8sbr2tDQAhWFiFQKHQa_CosQFgggMAA&url=http%3A%2F%2Fwww.matthewzeiler.com%2Fpubs%2Ficlr2013%2Ficlr2013.pdf&usg=AFQjCNGBzD7QvKjhVqEfTqx52q3Fgvz3IA)] 32 | - [ZFNet] Visualizing and Understanding Convolutional Networks. [[url](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjkzKeI29DQAhXCqlQKHZMCDRAQFggiMAA&url=http%3A%2F%2Fwww.cs.nyu.edu%2F~fergus%2Fpapers%2FzeilerECCV2014.pdf&usg=AFQjCNFRXYdMaGmpHegQP_bccZWSKDgmlw)] :star: 33 | 34 | ### Transfer learning 35 | 36 | - Active transfer learning for cross-system recommendation. [[pdf](http://www.cs.ust.hk/~qyang/Docs/2013/Zhao.pdf)] 37 | - Combating Negative Transfer From Predictive Distribution Differences. [[url](https://www.researchgate.net/publication/260586601_Combating_Negative_Transfer_From_Predictive_Distribution_Differences)] 38 | - Feature Ensemble Plus Sample Selection: Domain Adaptation for Sentiment Classification. [[pdf](http://www.nlpr.ia.ac.cn/2013papers/gjkw/gk107.pdf)] :star: 39 | - On handling negative transfer and imbalanced distributions in multiple source transfer learning. [[pdf](http://www.cse.buffalo.edu/DBGROUP/bioinformatics/papers/Liang_SDM13_SLW.pdf)] 40 | - Transfer feature learning with joint distribution adaptation. [[pdf](http://www.cv-foundation.org/openaccess/content_iccv_2013/papers/Long_Transfer_Feature_Learning_2013_ICCV_paper.pdf)] 41 | 42 | ### Deep Reinforcement Learning 43 | 44 | - Evolving large-scale neural networks for vision-based reinforcement learning. [[idsia](http://people.idsia.ch/~juergen/gecco2013torcs.pdf)] :star: 45 | - [Playing Atari with Deep Reinforcement Learning.](http://www.cnblogs.com/wangxiaocvpr/p/5601972.html) [[toronto](https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf)] :star: 46 | -------------------------------------------------------------------------------- /papers/2014.md: -------------------------------------------------------------------------------- 1 | ## 2014 2 | 3 | ### Deep Learning 4 | 5 | - A survey of multiple classifier systems as hybrid systems. [`science`](http://www.sciencedirect.com/science/article/pii/S156625351300047X) :star: 6 | - A survey on feature selection methods. [`science`](http://www.sciencedirect.com/science/article/pii/S0045790613003066) :star: 7 | - [DeepFaceVariant] [Deep Learning Face Representation from Predicting 10,000 Classes.](http://www.ifight.me/197/) [[pdf](http://mmlab.ie.cuhk.edu.hk/pdf/YiSun_CVPR14.pdf)] [[code](https://github.com/joyhuang9473/deepid-implementation)] :star: 8 | - Dropout: A Simple Way to Prevent Neural Networks from Overfitting. [[pdf](https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf)] 9 | - Generative Moment Matching Networks. [[arxiv](https://arxiv.org/abs/1502.02761)] [[code](https://github.com/yujiali/gmmn)] 10 | - [Inception V1] [Going Deeper with Convolutions](http://blog.csdn.net/u014114990/article/details/50370446). [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&uact=8&ved=0ahUKEwjHpOvi5NDQAhUCxLwKHU4BBM8QFgguMAE&url=https%3A%2F%2Fwww.cs.unc.edu%2F~wliu%2Fpapers%2FGoogLeNet.pdf&usg=AFQjCNHSEJVb0PWLBIG-Y-zWh9gRv9ehBQ)] :star: 11 | - Learning Longer Memory in Recurrent Neural Networks. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&uact=8&ved=0ahUKEwiOkqOu5dDQAhVFa7wKHc7pCdgQFggsMAE&url=https%3A%2F%2Farxiv.org%2Fpdf%2F1412.7753&usg=AFQjCNEz4_vREocEuriflTVFg0GrMmaqfw)] 12 | - Learning to Execute. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwiVoZuO5tDQAhWJwLwKHVouD40QFggdMAA&url=https%3A%2F%2Farxiv.org%2Fabs%2F1410.4615&usg=AFQjCNEXYyZHLwwTzovP3pHsWa_jxvWvEQ)] 13 | - [Multi-scale Orderless Pooling of Deep Convolutional Activation Features.](http://blog.csdn.net/happyer88/article/details/51418059) [`arxiv`](https://arxiv.org/abs/1403.1840) :star: 14 | - [Network In Network](http://blog.csdn.net/hjimce/article/details/50458190). [`arxiv`](https://arxiv.org/abs/1312.4400)] :star: 15 | - [OverFeat] [OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks.](http://blog.csdn.net/whiteinblue/article/details/43374195) [`arxiv`](https://arxiv.org/abs/1312.6229)] [`code`](https://github.com/sermanet/OverFeat) :star: 16 | - Recurrent Neural Network Regularization. [`arxiv`](https://arxiv.org/abs/1409.2329) [`tensorflow`](https://github.com/tensorflow/models/tree/master/tutorials/rnn/ptb) :star: 17 | - Show and Tell: A Neural Image Caption Generator.[[url](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&uact=8&ved=0ahUKEwjL6s6Xn47RAhVlqVQKHaynDI4QFggnMAE&url=%68%74%74%70%73%3a%2f%2f%61%72%78%69%76%2e%6f%72%67%2f%70%64%66%2f%31%34%31%31%2e%34%35%35%35&usg=AFQjCNEawcm4ZOK9ZVIgCjylPb2HY1UOug)] :star: 18 | - [SPPNet] [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition.](http://blog.csdn.net/whiteinblue/article/details/43415035) [`arxiv`](https://arxiv.org/abs/1406.4729) [`keras`](https://github.com/yhenon/keras-spp) :star: 19 | - Striving for Simplicity: The All Convolutional Net. [[arxiv](https://arxiv.org/abs/1412.6806)] :star: 20 | - Towards end-to-end speech recognition with recurrent neural networks.[[pdf](http://jmlr.org/proceedings/papers/v32/graves14.pdf)] :star: 21 | - [VGGNet] [Very Deep Convolutional Networks for Large-Scale Image Recognition](http://www.cnblogs.com/xuanyuyt/p/5743758.html). [`arxiv`](https://arxiv.org/abs/1409.1556) [`code`](https://gist.github.com/ksimonyan/211839e770f7b538e2d8) :star: 22 | - What Regularized Auto-Encoders Learn from the Data. [`arxiv`](https://arxiv.org/pdf/1211.4246.pdf) 23 | 24 | ### Generative learning 25 | 26 | - [GAN] [Generative Adversarial Nets.](http://blog.csdn.net/solomon1558/article/details/52549409) [`arxiv`](https://arxiv.org/abs/1406.2661) [`code`](https://github.com/goodfeli/adversarial) :star: 27 | - [CGNA] [Conditional Generative Adversarial Nets.](http://blog.csdn.net/solomon1558/article/details/52555083) [`arxiv`](https://arxiv.org/abs/1411.1784) [`code`](https://github.com/zhangqianhui/Conditional-Gans) :star: 28 | - Deep Visual-Semantic Alignments for Generating Image Descriptions. [`arxiv`](https://arxiv.org/abs/1412.2306) :star: 29 | - Explaining and Harnessing Adversarial Examples. [`arxiv`](https://arxiv.org/abs/1412.6572) 30 | - On distinguishability criteria for estimating generative models. [`arxiv`](https://arxiv.org/abs/1412.6515) 31 | - [VAE] Semi-Supervised Learning with Deep Generative Models. [`arxiv`](https://arxiv.org/abs/1406.5298) [`code`](https://github.com/dpkingma/nips14-ssl) [`tensorflow`](https://github.com/hwalsuklee/tensorflow-mnist-CVAE) :star: 32 | 33 | ### Attention and memory 34 | 35 | - End-to-end Continuous Speech Recognition using Attention-based Recurrent NN: First Results. [[arxiv](https://arxiv.org/abs/1412.1602)] 36 | - Memory Networks. [[arxiv](https://arxiv.org/abs/1410.3916)] :star: 37 | - [Multiple Object Recognition with Visual Attention.](http://www.cnblogs.com/wangxiaocvpr/p/5559961.html) [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&uact=8&ved=0ahUKEwjW5KK95tDQAhVEbbwKHU3yC40QFgguMAE&url=https%3A%2F%2Farxiv.org%2Fpdf%2F1412.7755&usg=AFQjCNEdl2iMZSeK_mYsIKs8HXm4yI6zKQ)] 38 | - [Attention In NLP First] [Neural Machine Translation by Jointly Learning to Align and Translate.](http://blog.csdn.net/u011414416/article/details/51057789) [[arxiv](https://arxiv.org/abs/1409.0473)] [[code](https://github.com/spro/torch-seq2seq-attention)] :star: 39 | - [First Memory Paper] Neural Turing Machines. [[arxiv](https://arxiv.org/abs/1410.5401)] :star: 40 | - [RAM] [Recurrent Models of Visual Attention.](http://www.cnblogs.com/wangxiaocvpr/p/5537454.html) [[arxiv](https://arxiv.org/abs/1406.6247)] [[tensorflow](https://github.com/jlindsey15/RAM)] :star: 41 | - [Seq2Seq] Sequence to Sequence Learning with Neural Networks. [[arxiv](https://arxiv.org/abs/1409.3215)] [[code](https://github.com/farizrahman4u/seq2seq)] :star: 42 | 43 | ### Deep Reinforcement Learning 44 | 45 | - Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning.[[url](http://papers.nips.cc/paper/5421-deep-learning-for-real-time-atari-game-play-using-offline-monte-carlo-tree-search-planning.pdf)] 46 | 47 | ### Transfer learning 48 | 49 | - Adaptation regularization: a general framework for transfer learning. [[pdf](http://www3.ntu.edu.sg/home/sinnopan/publications/[TKDE14]Adaptation%20Regularization%20A%20General%20Framework%20for%20Transfer%20Learning.pdf)] :star: 50 | - Heterogeneous Domain Adaptation for Multiple Classes. [[pdf](http://jmlr.org/proceedings/papers/v33/zhou14.pdf)] 51 | - How transferable are features in deep neural networks? [`arxiv`](https://arxiv.org/abs/1411.1792) 52 | - Hybrid heterogeneous transfer learning through deep learning. [[pdf](http://www.ntu.edu.sg/home/sinnopan/publications/[AAAI14]Hybrid%20Heterogeneous%20Transfer%20Learning%20through%20Deep%20Learning.pdf)] 53 | - Learning with Augmented Features for Supervised and Semi-supervised Heterogeneous Domain Adaptation. [[pdf](http://lxduan.info/papers/LiTPAMI2014.pdf)] 54 | - Machine learning for targeted display advertising: transfer learning in action. [[url](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwiZr8H5uY7RAhXJq1QKHUocC64QFggfMAA&url=http%3A%2F%2Fdstillery.com%2Fwp-content%2Fuploads%2F2014%2F05%2FMachine-learning_target-display.pdf&usg=AFQjCNGDcM3pAUJ9-ZL7i0ujCUIWHenABQ)] 55 | - Source Free Transfer Learning for Text Classification. [[pdf](http://www.cse.ust.hk/~yinz/SourceFreeTransferLearningforTextClassification.pdf)] 56 | 57 | ### Natural language process 58 | 59 | - [A Convolutional Neural Network for Modelling Sentences.](http://www.jeyzhang.com/cnn-apply-on-modelling-sentence.html) [`arxiv`](https://arxiv.org/abs/1404.2188) [`code`](https://github.com/FredericGodin/DynamicCNN) :star: 60 | - Automatic Construction and Natural-Language Description of Nonparametric Regression Models. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&uact=8&ved=0ahUKEwj3xcWb49DQAhWIw7wKHSXFCfEQFggrMAE&url=http%3A%2F%2Fwww.aaai.org%2Focs%2Findex.php%2FAAAI%2FAAAI14%2Fpaper%2FviewFile%2F8240%2F8564&usg=AFQjCNFyni0wwo38CsLRVtSPMm6BlL7QpA)] 61 | - [Convolutional Neural Networks for Sentence Classification.](https://arxiv.org/abs/1408.5882) [`arxiv`](https://arxiv.org/abs/1408.5882) [`tensorflow`](https://github.com/abhaikollara/CNN-Sentence-Classification) :star: 62 | - Distributed Representations of Sentences and Documents Generating Distribution. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwiulvPb49DQAhUGbrwKHeFRAlsQFggiMAA&url=http%3A%2F%2Fcs.stanford.edu%2F~quocle%2Fparagraph_vector.pdf&usg=AFQjCNESECVF_9eXAkAjfSqqHrqlxkVQgg)] :star: 63 | - Effective Use of Word Order for Text Categorization with Convolutional Neural Networks. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&uact=8&ved=0ahUKEwiIl7qS5NDQAhVD2LwKHct_CVYQFggpMAE&url=https%3A%2F%2Farxiv.org%2Fpdf%2F1412.1058&usg=AFQjCNHDPOYHMKWIhirkznqnLq_mw4CqMQ)] 64 | - Grammar as a Foreign Language. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&uact=8&ved=0ahUKEwitq9CI5dDQAhXCu7wKHTUIBiAQFggpMAE&url=https%3A%2F%2Fpapers.nips.cc%2Fpaper%2F5635-grammar-as-a-foreign-language.pdf&usg=AFQjCNELENZf9OsnZ6q0LexQYcbjCHBv0w)] 65 | - [GRU] [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](http://www.zmonster.me/notes/phrase_representation_using_rnn_encoder_decoder.html). [`arxiv`](https://arxiv.org/abs/1406.1078) :star: 66 | - On the Properties of Neural Machine Translation- Encoder-Decoder Approaches. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&uact=8&ved=0ahUKEwjqtZDc59DQAhUGyrwKHbhDBLUQFggsMAE&url=https%3A%2F%2Farxiv.org%2Fpdf%2F1409.1259&usg=AFQjCNG6_CJ8ZYMv5sx4K59mRIPpHlL-Yg)] 67 | - On Using Very Large Target Vocabulary for Neural Machine Translation. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&uact=8&ved=0ahUKEwiSle7659DQAhULTbwKHfaiBsoQFggsMAE&url=http%3A%2F%2Fwww.aclweb.org%2Fanthology%2FP15-1001&usg=AFQjCNFUabHMFw5X9gjg26vjoDljEd4s_g)] 68 | - Reading Text in the Wild with Convolutional Neural Networks. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&uact=8&ved=0ahUKEwiBlcrmn9PQAhXGW7wKHa6VAEwQFggsMAE&url=https%3A%2F%2Fwww.robots.ox.ac.uk%2F~vgg%2Fpublications%2F2016%2FJaderberg16%2Fjaderberg16.pdf&usg=AFQjCNG2V55rN1HOyhtSMLcHAyiuAYFl3A)] 69 | - [Seq2Seq] Sequence to Sequence Learning with Neural Networks. [`arxiv`](https://arxiv.org/abs/1409.3215) [`code`](https://github.com/farizrahman4u/seq2seq) :star: 70 | -------------------------------------------------------------------------------- /papers/2016/cv.md: -------------------------------------------------------------------------------- 1 | ## Computer vision 2 | 3 | - Accurate Image Super-Resolution Using Very Deep Convolutional Networks. [`arxiv`](https://arxiv.org/abs/1511.04587) 4 | - Action Recognition Based on Joint Trajectory Maps Using Convolutional Neural Networks.[`arxiv`](https://arxiv.org/abs/1611.02447) 5 | - Adult Content Recognition from Images Using a Mixture of Convolutional Neural Networks. [`arxiv`](https://arxiv.org/abs/1612.09506?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%253A+arxiv%252FQSXk+%2528ExcitingAds%2521+cs+updates+on+arXiv.org%2529) 6 | - Asynchronous Temporal Fields for Action Recognition. [`arxiv`](https://arxiv.org/abs/1612.06371) [`code`](https://github.com/gsig/temporal-fields/) 7 | - Automatic Portrait Segmentation for Image Stylization. [`pdf`](http://www.cse.cuhk.edu.hk/leojia/papers/portrait_eg16.pdf) [`code`](https://github.com/PetroWu/AutoPortraitMatting) 8 | - CMS-RCNN: Contextual Multi-Scale Region-based CNN for Unconstrained Face Detection. [`arxiv`](https://arxiv.org/abs/1606.05413) 9 | - Colorful Image Colorization. [`arxiv`](https://arxiv.org/abs/1603.08511v1) [`tensorflow`](https://github.com/nilboy/colorization-tf) :star: 10 | - [PCNN] Conditional Image Generation with PixelCNN Decoders. [`arxiv`](https://arxiv.org/abs/1606.05328) [`code`](https://github.com/kundan2510/pixelCNN) :star: 11 | - DA-RNN: Semantic Mapping with Data Associated Recurrent Neural Networks. [`arXiv`](https://arxiv.org/abs/1703.03098) [`code`](https://github.com/yuxng/DA-RNN) 12 | - [Use VGG19] Deep Feature Interpolation for Image Content Changes. [`arxiv`](https://arxiv.org/abs/1611.05507) [`tensorflow`](https://github.com/slang03/dfi-tensorflow) :star: 13 | - DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. [`arxiv`](https://arxiv.org/abs/1606.00915) :star: 14 | - Deep Learning Logo Detection with Data Expansion by Synthesising Context. [`arxiv`](https://arxiv.org/abs/1612.09322) 15 | - Deep Learning on Lie Groups for Skeleton-based Action Recognition. [`arxiv`](https://arxiv.org/abs/1612.05877) 16 | - Differential Geometry Boosts Convolutional Neural Networks for Object Detection. [`url`](http://www.cv-foundation.org/openaccess/content_cvpr_2016_workshops/w23/html/Wang_Differential_Geometry_Boosts_CVPR_2016_paper.html) 17 | - Efficient Action Detection in Untrimmed Videos via Multi-Task Learning. [`arxiv`](https://arxiv.org/abs/1612.07403) 18 | - ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation. [`arxiv`](https://arxiv.org/abs/1606.02147) [`tensorflow`](https://github.com/kwotsin/TensorFlow-ENet) 19 | - [EnhanceNet] EnhanceNet: Single Image Super-Resolution through Automated Texture Synthesis. [`arxiv`](https://arxiv.org/abs/1612.07919) :star: 20 | - Factorized Bilinear Models for Image Recognition. [`arxiv`](https://arxiv.org/abs/1611.05709) [`code`](https://github.com/lyttonhao/Factorized-Bilinear-Network) 21 | - Feature Pyramid Networks for Object Detection. [`arxiv`](https://arxiv.org/abs/1612.03144) [`code`](https://github.com/xmyqsh/FPN) 22 | - Finding Tiny Faces. [`arxiv`](https://arxiv.org/abs/1612.04402) [`tensorflow`](https://github.com//cydonia999/Tiny_Faces_in_Tensorflow) 23 | - Fully Convolutional Networks for Semantic Segmentation. [`arxiv`](https://arxiv.org/abs/1605.06211) :star: 24 | - Fully-Convolutional Siamese Networks for Object Tracking. [`arxiv`](https://arxiv.org/abs/1606.09549) [`tensorflow`](https://github.com/torrvision/siamfc-tf) 25 | - Full Resolution Image Compression with Recurrent Neural Networks. [`arxiv`](https://arxiv.org/abs/1608.05148) [`tensorflow`](https://github.com/tensorflow/models/tree/master/compression) :star: 26 | - Grad-CAM: Why did you say that? Visual Explanations from Deep Networks via Gradient-based Localization. [`arxiv`](https://arxiv.org/abs/1610.02391) [`tensorflow`](https://github.com/Ankush96/grad-cam.tensorflow) :star: 27 | - Hardware for Machine Learning: Challenges and Opportunities. [`arxiv`](https://arxiv.org/abs/1612.07625?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%253A+arxiv%252Fcs%252FCV+%2528ArXiv.cs.CV%2529) 28 | - Inception Recurrent Convolutional Neural Network for Object Recognition. [`arxiv`](https://arxiv.org/abs/1704.07709) 29 | - [Inception-V4] Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. [`arxiv`](https://arxiv.org/abs/1602.07261) :star: 30 | - Internet-Based Image Retrieval Using End-to-End Trained Deep Distributions. [`arxiv`](https://arxiv.org/abs/1612.07697) 31 | - Learning Non-Lambertian Object Intrinsics across ShapeNet Categories. [`arxiv`](https://arxiv.org/abs/1612.08510) 32 | - Learning Residual Images for Face Attribute Manipulation. [`arxiv`](https://arxiv.org/abs/1612.05363) 33 | - Maxmin convolutional neural networks for image classification. [`arxiv`](https://arxiv.org/abs/1610.07882) 34 | - Movie Description. [`arxiv`](https://arxiv.org/abs/1605.03705) 35 | - OctNet: Learning Deep 3D Representations at High Resolutions. [`arxiv`](https://arxiv.org/abs/1611.05009) [`code`](https://github.com/griegler/octnet) 36 | - Multivariate LSTM-FCNs for Time Series Classification. [`arxiv`](https://arxiv.org/abs/1611.05198) [`code`](https://github.com/kmaninis/OSVOS-PyTorch) 37 | - [PRNN] Pixel Recurrent Neural Networks. [`arxiv`](https://arxiv.org/pdf/1601.06759.pdf) :star: 38 | - Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space. [`arxiv`](https://arxiv.org/abs/1612.00005) 39 | - Physically-Based Rendering for Indoor Scene Understanding Using Convolutional Neural Networks. [`arxiv`](https://arxiv.org/abs/1612.07429) 40 | - PVANet: Lightweight Deep Neural Networks for Real-time Object Detection. [`arxiv`](https://arxiv.org/abs/1611.08588)[`code`](https://github.com/sanghoon/pva-faster-rcnn) 41 | - Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields. [`arxiv`](https://arxiv.org/abs/1611.08050) [`pytorch`](https://github.com//last-one/Pytorch_Realtime_Multi-Person_Pose_Estimation) 42 | - Richer Convolutional Features for Edge Detection. [`arxiv`](https://arxiv.org/abs/1612.02103) [`code`](https://github.com/yun-liu/rcf) 43 | - [R-FCN] [R-FCN: Object Detection via Region-based Fully Convolutional Networks](http://blog.csdn.net/u012361214/article/details/51507590). [`arxiv`](https://arxiv.org/abs/1605.06409) [`code`](https://github.com/Orpine/py-R-FCN) :star: 44 | - Robust LSTM-Autoencoders for Face De-Occlusion in the Wild. [`arxiv`](https://arxiv.org/abs/1612.08534) 45 | - Semantic Video Segmentation by Gated Recurrent Flow Propagation.[`arxiv`](https://arxiv.org/abs/1612.08871) 46 | - Shape Completion using 3D-Encoder-Predictor CNNs and Shape Synthesis. [`arxiv`](https://arxiv.org/abs/1612.00101) [`torch`](https://github.com/angeladai/cnncomplete) :star: 47 | - Spatially Adaptive Computation Time for Residual Networks. [`arxiv`](https://arxiv.org/abs/1612.02297) [`tensorflow`](https://github.com/mfigurnov/sact) 48 | - [SqueezeNet] [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size.](http://blog.csdn.net/human_recognition/article/details/51902285) [`arxiv`](https://arxiv.org/abs/1602.07360) [`code`](https://github.com/songhan/SqueezeNet-Deep-Compression) :star: 49 | - Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge. [`arxiv`](http://arxiv.org/abs/1609.06647) [`tensorflow`](https://github.com/tensorflow/models/tree/master/im2txt) :star: 50 | - sk_p- a neural program corrector for MOOCs. [`url`](http://dl.acm.org/citation.cfm?id=2989222) 51 | - Stacked Hourglass Networks for Human Pose Estimation. [`arxiv`](https://arxiv.org/abs/1603.06937) [`code`](https://github.com/anewell/pose-hg-train) :star: 52 | - The Predictron: End-To-End Learning and Planning. [`arxiv`](https://arxiv.org/abs/1612.08810) [`tensorflow`](https://github.com/zhongwen/predictron) :star: 53 | - Unsupervised Cross-Domain Image Generation. [`arxiv`](https://arxiv.org/abs/1611.02200) [`tensorflow`](https://github.com/yunjey/dtn-tensorflow) 54 | - Unsupervised Learning for Physical Interaction through Video Prediction. [`arxiv`](https://arxiv.org/abs/1605.07157) [`tensorflow`](https://github.com/tensorflow/models/tree/master/video_prediction) :star: 55 | - Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles. [`arxiv`](https://arxiv.org/pdf/1603.09246.pdf) 56 | - Video Pixel Networks. [`arxiv`](https://arxiv.org/abs/1610.00527) 57 | - Visual Genome-Connecting Language and Vision Using Crowdsourced Dense Image Annotations. [`arxiv`](https://arxiv.org/abs/1602.07332) :star: 58 | - [WaveNet] WaveNet- A Generative Model For Raw Audio. [`arxiv`](https://arxiv.org/abs/1609.03499) :star: 59 | - YOLO9000: Better, Faster, Stronger. [`arxiv`](https://arxiv.org/abs/1612.08242) [`keras`](https://github.com/allanzelener/YAD2K) :star: 60 | -------------------------------------------------------------------------------- /papers/2016/nlp.md: -------------------------------------------------------------------------------- 1 | ## natural language process 2 | 3 | - [return to home](../../README.md) 4 | 5 | ### NLP 6 | 7 | - Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond. [`pdf`](https://aclweb.org/anthology/K/K16/K16-1028.pdf) 8 | - AC-BLSTM: Asymmetric Convolutional Bidirectional LSTM Networks for Text Classification. [`arxiv`](https://arxiv.org/abs/1611.01884) 9 | - Achieving Human Parity in Conversational Speech Recognition. [`arxiv`](https://arxiv.org/abs/1610.05256) 10 | - A General Framework for Content-enhanced Network Representation Learning. [`arxiv`](https://arxiv.org/abs/1610.02906) 11 | - A Hybrid Geometric Approach for Measuring Similarity Level Among Documents and Document Clustering. [`pdf`](http://ieeexplore.ieee.org/document/7474366/?reload=true) [`code`](https://github.com/taki0112/Vector_Similarity) 12 | - A Joint Many-Task Model- Growing a Neural Network for Multiple NLP Tasks. [`arxiv`](https://arxiv.org/abs/1611.01587) 13 | - A Semisupervised Approach for Language Identification based on Ladder Networks. [`pdf`](http://www.eng.biu.ac.il/goldbej/files/2012/05/Odyssey_2016_paper.pdf) 14 | - A Simple, Fast Diverse Decoding Algorithm for Neural Generation. [`arxiv`](https://arxiv.org/abs/1611.08562) 15 | - Aspect Level Sentiment Classification with Deep Memory Network. [`arxiv`](https://arxiv.org/abs/1605.08900) [`tensorflow`](https://github.com/endymecy/transwarp-nlp) :star: 16 | - A Strong Baseline for Learning Cross-Lingual Word Embeddings from Sentence Alignments. [`arxiv`](https://arxiv.org/abs/1608.05426) 17 | - Cached Long Short-Term Memory Neural Networks for Document-Level Sentiment Classification. [`arxiv`](https://arxiv.org/abs/1610.04989) 18 | - Character-level and Multi-channel Convolutional Neural Networks for Large-scale Authorship Attribution. [`arxiv`](https://arxiv.org/abs/1609.06686) 19 | - COCO-Text-Dataset and Benchmark for Text Detection and Recognition in Natural Images. [`pdf`](http://sunw.csail.mit.edu/papers/01_Veit_SUNw.pdf) 20 | - Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks. [`arxiv`](https://arxiv.org/abs/1611.00454) 21 | - Collaborative Recurrent Neural Networks for Dynamic Recommender Systems. [`pdf`](https://infoscience.epfl.ch/record/222477/files/ko101.pdf) [`tensorflow`](https://github.com/lca4/collaborative-rnn) 22 | - Context-aware Natural Language Generation with Recurrent Neural Networks. [`arxiv`](https://arxiv.org/abs/1611.09900) 23 | - [CLSTM] Contextual LSTM models for Large scale NLP tasks. [`pdf`](http://www.csl.sri.com/users/shalini/clstm_dlkdd16.pdf) :star: 24 | - Deep Biaffine Attention for Neural Dependency Parsing. [`pdf`](https://openreview.net/pdf?id=Hk95PK9le) [`code`](https://github.com/tdozat/Parser) :star: 25 | - Deep Semi-Supervised Learning with Linguistically Motivated Sequence Labeling Task Hierarchies. [`arxiv`](https://arxiv.org/abs/1612.09113) 26 | - Deep Neural Networks for YouTube Recommendations. [`pdf`](https://static.googleusercontent.com/media/research.google.com/zh-CN//pubs/archive/45530.pdf) 27 | - Detecting Text in Natural Image with Connectionist Text Proposal Network. [`arxiv`](https://arxiv.org/abs/1609.03605) [`code`](https://github.com/qingswu/CTPN) 28 | - Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models. [`arxiv`](https://arxiv.org/abs/1610.02424) 29 | - Efficient Character-level Document Classification by Combining Convolution and Recurrent Layers. [`arxiv`](https://arxiv.org/abs/1602.00367) 30 | - End-to-End Answer Chunk Extraction and Ranking for Reading Comprehension. [`arxiv`](https://arxiv.org/abs/1610.09996) 31 | - End-to-End Multi-View Networks for Text Classification. [`arxiv`](https://arxiv.org/abs/1704.05907) 32 | - End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF. [`arxiv`](https://arxiv.org/abs/1603.01354) :star: 33 | - Enhancing and Combining Sequential and Tree LSTM for Natural Language Inference. [`arxiv`](https://arxiv.org/abs/1609.06038) 34 | - Fully Convolutional Instance-aware Semantic Segmentation. [`arxiv`](https://arxiv.org/abs/1611.07709) [`code`](https://github.com/msracver/FCIS) 35 | - Generative Deep Neural Networks for Dialogue: A Short Review. [`arxiv`](https://arxiv.org/abs/1611.06216) 36 | - Generating Factoid Questions With Recurrent Neural Networks- The 30M Factoid Question-Answer Corpus. [`pdf`](https://aclweb.org/anthology/P/P16/P16-1056.pdf) :star: 37 | - Globally Normalized Transition-Based Neural Networks. [`arxiv`](https://arxiv.org/abs/1603.06042) [`tensorflow`](https://github.com/tensorflow/models/tree/master/syntaxnet) :star: 38 | - GraphNet: Recommendation system based on language and network structure. [`pdf`](https://web.stanford.edu/class/cs224n/reports/2758630.pdf) 39 | - How NOT To Evaluate Your Dialogue System. [`arxiv`](https://arxiv.org/abs/1603.08023) :star: 40 | - Inducing Multilingual Text Analysis Tools Using Bidirectional Recurrent Neural Networks. [`pdf`](https://hal.archives-ouvertes.fr/hal-01374205/document) 41 | - Key-Value Memory Networks for Directly Reading Documents. [`pdf`](https://arxiv.org/pdf/1606.03126.pdf) 42 | - Learning Distributed Representations of Sentences from Unlabelled Data. [`arxiv`](https://arxiv.org/abs/1602.03483) :star: 43 | - Learning End-to-End Goal-Oriented Dialog. [`arxiv`](https://arxiv.org/abs/1605.07683) [`tensorflow`](https://github.com/vyraun/chatbot-MemN2N-tensorflow) :star: 44 | - Learning Recurrent Span Representations for Extractive Question Answering. [`arxiv`](https://arxiv.org/abs/1611.01436) 45 | - Learning to Compose Neural Networks for Question Answering. [`arxiv`](https://arxiv.org/abs/1601.01705) :star: 46 | - Learning to Translate in Real-time with Neural Machine Translation. [`arxiv`](https://arxiv.org/abs/1610.00388) 47 | - Linguistically Regularized LSTMs for Sentiment Classification. [`arxiv`](https://arxiv.org/abs/1611.03949) 48 | - Long Short-Term Memory-Networks for Machine Reading. [`url`](https://aclweb.org/anthology/D16-1053) :star: 49 | - [lda2vec] Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec. [`arxiv`](https://arxiv.org/abs/1605.02019) [`code`](https://github.com/cemoody/lda2vec) [`tensorflow`](https://github.com/meereeum/lda2vec-tf) :star: 50 | - Modeling Coverage for Neural Machine Translation. [`pdf`](http://www.hangli-hl.com/uploads/3/4/4/6/34465961/tu_et_al_2016.pdf) :star: 51 | - Multilingual Part-of-Speech Tagging with Bidirectional Long Short-Term Memory Models and Auxiliary Loss. [`pdf`](https://www.aclweb.org/anthology/P/P16/P16-2067.pdf) [`code`](https://github.com/bplank/bilstm-aux) :star: 52 | - MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving. [`arxiv`](https://arxiv.org/abs/1612.07695) :star: 53 | - Neural Architectures for Fine-grained Entity Type Classification. [`arxiv`](https://arxiv.org/abs/1606.01341) 54 | - Neural Architectures for Named Entity Recognition. [`arxiv`](https://arxiv.org/abs/1603.01360) :star: 55 | - Neural Emoji Recommendation in Dialogue Systems.[`arxiv`](https://arxiv.org/abs/1612.04609) 56 | - Neural Paraphrase Generation with Stacked Residual LSTM Networks. [`arxiv`](https://arxiv.org/abs/1610.03098) 57 | - Neural Semantic Encoders. [`arxiv`](https://arxiv.org/abs/1607.04315) 58 | - Neural Variational Inference for Text Processing. [`arxiv`](https://arxiv.org/pdf/1511.06038) :star: 59 | - Online Segment to Segment Neural Transduction. [`arxiv`](https://arxiv.org/abs/1609.08194) 60 | - On Random Weights for Texture Generation in One Layer Neural Networks.[`arxiv`](https://arxiv.org/abs/1612.06070?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%253A+arxiv%252FQSXk+%2528ExcitingAds%2521+cs+updates+on+arXiv.org%2529) 61 | - Parallelizing Word2Vec in Shared and Distributed Memory.[`arxiv`](https://arxiv.org/abs/1604.04661) [`code`](https://github.com/IntelLabs/pWord2Vec) 62 | - Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences. [`arxiv`](https://arxiv.org/abs/1610.09513) [`code`](https://github.com/dannyneil/public_plstm) 63 | - Recurrent Neural Network Grammars. [`arxiv`](https://arxiv.org/abs/1602.07776) :star: 64 | - Reading Wikipedia to Answer Open-Domain Questions. [`arxiv`](https://arxiv.org/abs/1704.00051) [`pytorch`](https://github.com/facebookresearch/DrQA) 65 | - ReasoNet: Learning to Stop Reading in Machine Comprehension. [`arxiv`](https://arxiv.org/abs/1609.05284) 66 | - Sentence Level Recurrent Topic Model- Letting Topics Speak for Themselves. [`arxiv`](https://arxiv.org/abs/1604.02038) 67 | - Sentence-Level Grammatical Error Identification as Sequence-to-Sequence Correction. [`pdf`](https://aclweb.org/anthology/W/W16/W16-0528.pdf) 68 | - Sentence Ordering using Recurrent Neural Networks. [`arxiv`](https://arxiv.org/abs/1611.02654) 69 | - Sequence to Backward and Forward Sequences: A Content-Introducing Approach to Generative Short-Text Conversation. [`arxiv`](https://arxiv.org/abs/1607.00970) 70 | - Sequential Match Network: A New Architecture for Multi-turn Response Selection in Retrieval-based Chatbots. [`arxiv`](https://arxiv.org/abs/1612.01627) 71 | - Structured Sequence Modeling with Graph Convolutional Recurrent Networks. [`arxiv`](https://arxiv.org/abs/1612.07659) 72 | - [TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency.](http://weibo.com/ttarticle/p/show?id=2309404086416278721142) [`arxiv`](https://arxiv.org/abs/1611.01702) 73 | - Tracking the World State with Recurrent Entity Networks . [`arxiv`](https://arxiv.org/abs/1612.03969) :star: 74 | - Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling. [`arxiv`](https://arxiv.org/abs/1611.01462) [`code`](https://github.com/icoxfog417/tying-wv-and-wc) 75 | - Tweet2Vec: Learning Tweet Embeddings Using Character-level CNN-LSTM Encoder-Decoder.[`arxiv`](https://arxiv.org/abs/1607.07514) [`code`](https://github.com/soroushv/Tweet2Vec) 76 | - Unsupervised Learning of Sentence Representations using Convolutional Neural Networks. [`arxiv`](https://arxiv.org/abs/1611.07897) 77 | - Unsupervised neural and Bayesian models for zero-resource speech processing. [`arxiv`](https://arxiv.org/abs/1701.00851) 78 | - Unsupervised Pretraining for Sequence to Sequence Learning. [`arxiv`](https://arxiv.org/abs/1611.02683) 79 | - UTCNN: a Deep Learning Model of Stance Classificationon on Social Media Text. [`arxiv`](https://arxiv.org/abs/1611.03599) 80 | - Very Deep Convolutional Networks for Natural Language Processing. [`arxiv`](https://arxiv.org/pdf/1606.01781.pdf) :star: 81 | - Visual Dialog. [`arxiv`](https://arxiv.org/abs/1611.08669) [`code`](https://github.com/Cloud-CV/visual-chatbot) :star: 82 | - Wav2Letter: an End-to-End ConvNet-based Speech Recognition System. [`arxiv`](https://arxiv.org/abs/1609.03193) 83 | - [Wide & Deep Learning for Recommender Systems.](http://blog.csdn.net/dinosoft/article/details/52581368) [`arxiv`](https://arxiv.org/abs/1606.07792) [`tensorflow`](https://www.tensorflow.org/tutorials/wide_and_deep/) :star: 84 | 85 | ### Generative learning 86 | 87 | - Adversarial Training Methods for Semi-Supervised Text Classification. [`arxiv`](https://arxiv.org/abs/1605.07725) 88 | - Aspect Level Sentiment Classification with Deep Memory Network. [`arxiv`](https://arxiv.org/pdf/1605.08900.pdf) 89 | - Generative Adversarial Text to Image Synthesis. [`arxiv`](https://arxiv.org/abs/1605.05396) :star: 90 | - Modeling documents with Generative Adversarial Networks. [`arxiv`](https://arxiv.org/abs/1612.09122) 91 | - [StackGAN] StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks. [`arxiv`](https://arxiv.org/abs/1612.03242) [`code`](https://github.com/hanzhanggit/StackGAN) :star: 92 | 93 | ### Attention and memory 94 | 95 | - ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs. [`arxiv`](https://arxiv.org/abs/1512.05193) [`tensorflow`](https://github.com/galsang/ABCNN) 96 | - A Context-aware Attention Network for Interactive Question Answering. [`url`](https://openreview.net/pdf?id=SkyQWDcex) 97 | - A Decomposable Attention Model for Natural Language Inference. [`arxiv`](https://arxiv.org/abs/1606.01933) [`code`](https://github.com/harvardnlp/decomp-attn) 98 | - A self-attentive sentence embedding. [`pdf`](https://openreview.net/pdf?id=BJC_jUqxe) 99 | - Aspect Level Sentiment Classification with Deep Memory Network. [`arxiv`](https://arxiv.org/abs/1605.08900) [`code`](http://nlp.stanford.edu/sentiment/code.html) :star: 100 | - AttSum: Joint Learning of Focusing and Summarization with Neural Attention. [`arxiv`](https://arxiv.org/abs/1604.00125) 101 | - Attention-over-Attention Neural Networks for Reading Comprehension. [`arxiv`](https://arxiv.org/abs/1607.04423) [`code`](https://github.com/OlavHN/attention-over-attention) 102 | - Collective Entity Resolution with Multi-Focal Attention. [`pdf`](https://www.aclweb.org/anthology/P/P16/P16-1059.pdf) 103 | - [Gated-Attention Readers for Text Comprehension.](https://theneuralperspective.com/2017/01/19/gated-attention-readers-for-text-comprehension/) [`pdf`](https://openreview.net/pdf?id=HkcdHtqlx) 104 | - Hierarchical Attention Networks for Document Classification. [`pdf`](https://www.cs.cmu.edu/~diyiy/docs/naacl16.pdf) [`tensorflow`](https://github.com/ematvey/deep-text-classifier) :star: 105 | - Hierarchical Memory Networks for Answer Selection on Unknown Words. [`arxiv`](https://arxiv.org/abs/1609.08843) 106 | - Implicit Distortion and Fertility Models for Attention-based Encoder-Decoder NMT Model. [`arxiv`](https://arxiv.org/abs/1601.03317) 107 | - Improving Attention Modeling with Implicit Distortion and Fertility for Machine Translation. [`pdf`](https://www.aclweb.org/anthology/C/C16/C16-1290.pdf) 108 | - Iterative Alternating Neural Attention for Machine Reading. [`arxiv`](https://arxiv.org/pdf/1606.02245.pdf) 109 | - Joint CTC-Attention based End-to-End Speech Recognition using Multi-task Learning. [`arxiv`](https://arxiv.org/abs/1609.06773) 110 | - Key-Value Memory Networks for Directly Reading Documents. [`arxiv`](https://arxiv.org/abs/1606.03126) 111 | - Knowledge as a Teacher: Knowledge-Guided Structural Attention Networks. [`arxiv`](https://arxiv.org/abs/1609.03286) 112 | - Language to Logical Form with Neural Attention. [`pdf`](http://homepages.inf.ed.ac.uk/s1478528/acl16-lang2logic-slides.pdf) :star: 113 | - Learning Natural Language Inference using Bidirectional LSTM model and Inner-Attention. [`arxiv`](https://arxiv.org/abs/1605.09090) 114 | - Lexicon Integrated CNN Models with Attention for Sentiment Analysis. [`arxiv`](https://arxiv.org/abs/1610.06272) 115 | - Memory-enhanced Decoder for Neural Machine Translation. [`arxiv`](https://arxiv.org/abs/1606.02003) 116 | - Neural Language Correction with Character-Based Attention. [`arxiv`](https://arxiv.org/abs/1603.09727) 117 | - Visualizing and Understanding Curriculum Learning for Long Short-Term Memory Networks. [`arxiv`](https://arxiv.org/abs/1611.06204) 118 | 119 | ### Neural Machine Translation 120 | 121 | - Achieving Open Vocabulary Neural Machine Translation with Hybrid Word-Character Models. [`arxiv`](https://arxiv.org/abs/1604.00788) :star: 122 | - A Character-level Decoder without Explicit Segmentation for Neural Machine Translation. [`pdf`](https://www.aclweb.org/anthology/P/P16/P16-1160.pdf) :star: 123 | - A Convolutional Encoder Model for Neural Machine Translation. [`arxiv`](https://arxiv.org/abs/1611.02344) [`pytorch`](https://github.com//pravarmahajan/cnn-encoder-nmt) 124 | - Character-based Neural Machine Translation. [`arxiv`](https://arxiv.org/abs/1511.04586) :star: 125 | - Context-Dependent Word Representation for Neural Machine Translation. [`arxiv`](https://arxiv.org/pdf/1607.00578.pdf) 126 | - Convolutional Encoders for Neural Machine Translation. [`pdf`](https://cs224d.stanford.edu/reports/LambAndrew.pdf) 127 | - Deep Recurrent Models with Fast-Forward Connections for Neural Machine Translatin. [`arxiv`](https://arxiv.org/pdf/1606.04199.pdf) 128 | - Dual Learning for Machine Translation. [`pdf`](https://papers.nips.cc/paper/6469-dual-learning-for-machine-translation.pdf) 129 | - Fast Domain Adaptation for Neural Machine Translation. [`arxiv`](https://arxiv.org/abs/1612.06897) 130 | - Fully Character-Level Neural Machine Translation without Explicit Segmentation. [`pdf`](https://arxiv.org/pdf/1610.03017.pdf) :star: 131 | - Google's Multilingual Neural Machine Translation System- Enabling Zero-Shot Translation. [`arxiv`](https://arxiv.org/abs/1611.04558) 132 | - Google's Neural Machine Translation System- Bridging the Gap between Human and Machine Translation. [`arxiv`](https://arxiv.org/abs/1609.08144) :star: 133 | - How Grammatical is Character-level Neural Machine Translation? Assessing MT Quality with Contrastive Translation Pairs. [`arxiv`](https://arxiv.org/abs/1612.04629) 134 | - Interactive Attention for Neural Machine Translation. [`arxiv`](https://arxiv.org/abs/1610.05011) 135 | - Multimodal Attention for Neural Machine Translation. [`arxiv`](https://arxiv.org/abs/1609.03976) 136 | - Multi-Way, Multilingual Neural Machine Translation with a Shared Attention Mechanism. [`arxiv`](https://arxiv.org/pdf/1601.01073.pdf) :star: 137 | - Modeling Coverage for Neural Machine Translation. [`pdf`](http://www.hangli-hl.com/uploads/3/4/4/6/34465961/tu_et_al_2016.pdf) :star: 138 | - Neural Machine Translation in Linear Time. [`arxiv`](https://arxiv.org/abs/1610.10099) 139 | - Neural Network Translation Models for Grammatical Error Correction. [`arxiv`](https://arxiv.org/pdf/1606.00189.pdf) 140 | - Neural Machine Translation with Latent Semantic of Image and Text. [`arxiv`](https://arxiv.org/abs/1611.08459) 141 | - Neural Machine Translation with Pivot Languages. [`arxiv`](https://arxiv.org/abs/1611.04928) 142 | - Neural Machine Translation with Recurrent Attention Modeling. [`arxiv`](https://arxiv.org/abs/1607.05108) 143 | - Neural Machine Translation with Supervised Attention. [`pdf`](https://www.aclweb.org/anthology/C/C16/C16-1291.pdf) 144 | - Recurrent Neural Machine Translation.[`arxiv`](https://arxiv.org/abs/1607.08725) 145 | - Semi-Supervised Learning for Neural Machine Translation. [`pdf`](http://iiis.tsinghua.edu.cn/~weixu/files/acl2016_chengyong.pdf) 146 | - Temporal Attention Model for Neural Machine Translation. [`arxiv`](https://arxiv.org/abs/1608.02927) 147 | - Zero-Resource Translation with Multi-Lingual Neural Machine Translation. [`arxiv`](https://arxiv.org/pdf/1606.04164.pdf) 148 | 149 | ### Neural Language Model 150 | 151 | - Character-Aware Neural Language Models. [`pdf`](http://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/download/12489/12017) :star: 152 | - Character-Level Language Modeling with Hierarchical Recurrent Neural Networks. [`arxiv`](https://arxiv.org/abs/1609.03777) 153 | - Coherent Dialogue with Attention-based Language Models. [`arxiv`](https://arxiv.org/abs/1611.06997) 154 | - Joint Online Spoken Language Understanding and Language Modeling with Recurrent Neural Networks. [`arxiv`](https://arxiv.org/abs/1609.01462) 155 | - Improving neural language models with a continuous cache. [`pdf`](https://openreview.net/pdf?id=B184E5qee) 156 | - Language Modeling with Gated Convolutional Networks. [`arxiv`](https://arxiv.org/abs/1612.08083) [`tensorflow`](https://github.com/anantzoid/Language-Modeling-GatedCNN) :star: 157 | - Scalable Bayesian Learning of Recurrent Neural Networks for Language Modeling. [`arxiv`](https://arxiv.org/abs/1611.08034) 158 | - Recurrent Memory Networks for Language Modeling. [`arxiv`](https://arxiv.org/pdf/1601.01272.pdf) 159 | -------------------------------------------------------------------------------- /papers/2016/rl.md: -------------------------------------------------------------------------------- 1 | ## deep reinforcement learning 2 | 3 | - [return to home](../../README.md) 4 | 5 | ### papers 6 | 7 | - Asynchronous Methods for Deep Reinforcement Learning. [[arxiv](http://arxiv.org/abs/1602.01783)] :star: 8 | - Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning, E. Parisotto, et al., *ICLR*. [[arxiv](http://arxiv.org/abs/1511.06342)] 9 | - A New Softmax Operator for Reinforcement Learning.[[url](https://128.84.21.199/abs/1612.05628?context=cs)] 10 | - Benchmarking Deep Reinforcement Learning for Continuous Control, Y. Duan et al., *ICML*. [[arxiv](https://arxiv.org/abs/1604.06778)] 11 | - Better Computer Go Player with Neural Network and Long-term Prediction, Y. Tian et al., *ICLR*. [[arxiv](http://arxiv.org/abs/1511.06410)] 12 | - Deep Reinforcement Learning in Parameterized Action Space, M. Hausknecht et al., *ICLR*. [[arxiv](http://arxiv.org/abs/1511.04143)] 13 | - Curiosity-driven Exploration in Deep Reinforcement Learning via Bayesian Neural Networks, R. Houthooft et al., *arXiv*. [[url](http://arxiv.org/abs/1605.09674)] 14 | - Control of Memory, Active Perception, and Action in Minecraft, J. Oh et al., *ICML*. [[arxiv](http://arxiv.org/abs/1605.09128)] 15 | - Continuous Deep Q-Learning with Model-based Acceleration, S. Gu et al., *ICML*. [[arxiv](http://arxiv.org/abs/1603.00748)] 16 | - Continuous control with deep reinforcement learning. [[arxiv](http://arxiv.org/abs/1509.02971)] :star: 17 | - Deep Successor Reinforcement Learning. [[arxiv](http://arxiv.org/abs/1606.02396)] 18 | - Dynamic Frame skip Deep Q Network, A. S. Lakshminarayanan et al., *IJCAI Deep RL Workshop*. [[arxiv](http://arxiv.org/abs/1605.05365)] 19 | - Deep Exploration via Bootstrapped DQN. [[arxiv](http://arxiv.org/abs/1602.04621)] :star: 20 | - Deep Reinforcement Learning for Dialogue Generation. [[arxiv](https://arxiv.org/abs/1606.01541)] [`tensorflow`](https://github.com/BigPlay/Deep-Reinforcement-Learning-for-Dialogue-Generation-in-tensorflow) 21 | - Deep Reinforcement Learning in Parameterized Action Space. [[arxiv](http://arxiv.org/abs/1511.04143)] :star: 22 | - Deep Reinforcement Learning with Successor Features for Navigation across Similar Environments.[[url](https://scirate.com/arxiv/1612.05533)] 23 | - Designing Neural Network Architectures using Reinforcement Learning. [`arxiv`](https://arxiv.org/abs/1611.02167) [`code`](https://bowenbaker.github.io/metaqnn/) 24 | - Dialogue manager domain adaptation using Gaussian process reinforcement learning. [[arxiv](https://arxiv.org/abs/1609.02846)] 25 | - End-to-End Reinforcement Learning of Dialogue Agents for Information Access. [[arxiv](https://arxiv.org/abs/1609.00777)] 26 | - Generating Text with Deep Reinforcement Learning. [[arxiv](https://arxiv.org/abs/1510.09202)] 27 | - Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization, C. Finn et al., *arXiv*. [[arxiv](http://arxiv.org/abs/1603.00448)] 28 | - Hierarchical Reinforcement Learning using Spatio-Temporal Abstractions and Deep Neural Networks, R. Krishnamurthy et al., *arXiv*. [[arxiv](https://arxiv.org/abs/1605.05359)] 29 | - Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation, T. D. Kulkarni et al., *arXiv*. [[arxiv](https://arxiv.org/abs/1604.06057)] 30 | - Hierarchical Object Detection with Deep Reinforcement Learning. [[arxiv](https://arxiv.org/abs/1611.03718)] 31 | - High-Dimensional Continuous Control Using Generalized Advantage Estimation, J. Schulman et al., *ICLR*. [[arxiv](http://arxiv.org/abs/1506.02438)] 32 | - Increasing the Action Gap: New Operators for Reinforcement Learning, M. G. Bellemare et al., *AAAI*. [[arxiv](http://arxiv.org/abs/1512.04860)] 33 | - Interactive Spoken Content Retrieval by Deep Reinforcement Learning. [[arxiv](https://arxiv.org/abs/1609.05234)] 34 | - Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection, S. Levine et al., *arXiv*. [[url](http://arxiv.org/abs/1603.02199)] 35 | - Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks, J. N. Foerster et al., *arXiv*. [[url](http://arxiv.org/abs/1602.02672)] 36 | - Learning to compose words into sentences with reinforcement learning. [[url](https://www.google.com.hk/url?sa=t&rct=j&q=&esrc=s&source=web&cd=3&cad=rja&uact=8&ved=0ahUKEwim56OJ1I_RAhUJi1QKHcDRAEYQFgg2MAI&url=https%3A%2F%2Fwww.reddit.com%2Fr%2FMachineLearning%2Fcomments%2F5b373g%2Fr_learning_to_compose_words_into_sentences_with%2F&usg=AFQjCNFBoour5fTqAiKQF1NXNon2e-j9pA)] 37 | - Loss is its own Reward: Self-Supervision for Reinforcement Learning.[[arxiv](https://arxiv.org/abs/1612.07307)] 38 | - Model-Free Episodic Control. [[arxiv](http://arxiv.org/abs/1606.04460)] 39 | - Mastering the game of Go with deep neural networks and tree search. [[nature](http://www.nature.com/nature/journal/v529/n7587/full/nature16961.html)] :star: 40 | - MazeBase: A Sandbox for Learning from Games .[[arxiv](http://arxiv.org/abs/1511.07401)] 41 | - [Neural Architecture Search with Reinforcement Learning.](https://mp.weixin.qq.com/s?__biz=MzI0ODcxODk5OA==&mid=2247483966&idx=1&sn=e3fde0461e10e220aca322ca9395958c) [[pdf](https://openreview.net/pdf?id=r1Ue8Hcxg)] 42 | - Neural Combinatorial Optimization with Reinforcement Learning. [[arxiv](https://arxiv.org/abs/1611.09940)] 43 | - Non-Deterministic Policy Improvement Stabilizes Approximated Reinforcement Learning. [[url](https://ewrl.files.wordpress.com/2016/11/ewrl13-2016-submission_2.pdf)] 44 | - Online Sequence-to-Sequence Active Learning for Open-Domain Dialogue Generation. *arXiv*. [[arxiv](https://arxiv.org/abs/1612.03929)] 45 | - Policy Distillation, A. A. Rusu et at., *ICLR*. [[arxiv](http://arxiv.org/abs/1511.06295)] 46 | - Prioritized Experience Replay. [[arxiv](http://arxiv.org/abs/1511.05952)] :star: 47 | - Reinforcement Learning Using Quantum Boltzmann Machines. [[arxiv](https://arxiv.org/abs/1612.05695)] 48 | - Safe and Efficient Off-Policy Reinforcement Learning, R. Munos et al.[[arxiv](https://arxiv.org/abs/1606.02647)] 49 | - [Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving.](https://zhuanlan.zhihu.com/p/25673276) [[arxiv](https://arxiv.org/abs/1610.03295)] 50 | - Sample-efficient Deep Reinforcement Learning for Dialog Control. [[url](https://scirate.com/arxiv/1612.06000)] 51 | - Self-Correcting Models for Model-Based Reinforcement Learning.[[url](https://scirate.com/arxiv/1612.06018)] 52 | - Unifying Count-Based Exploration and Intrinsic Motivation. [[arxiv](https://arxiv.org/abs/1606.01868)] 53 | - Value Iteration Networks. [[arxiv](http://arxiv.org/abs/1602.02867)] 54 | -------------------------------------------------------------------------------- /papers/2017/rl.md: -------------------------------------------------------------------------------- 1 | ## deep reinforcement learning 2 | 3 | ### papers 4 | 5 | - [A Deep Reinforcement Learning Chatbot.](https://mp.weixin.qq.com/s/TpRXxP25-3uqpgC9CBi-3Q) [`arxiv`](https://arxiv.org/abs/1709.02349) 6 | - A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem. [`arxiv`](https://arxiv.org/abs/1706.10059) [`code`](https://github.com//ZhengyaoJiang/PGPortfolio) 7 | - A Deep Reinforced Model for Abstractive Summarization. [`arxiv`](https://arxiv.org/abs/1705.04304) 8 | - A Distributional Perspective on Reinforcement Learning. [`arxiv`](https://arxiv.org/abs/1707.06887) 9 | - A Laplacian Framework for Option Discovery in Reinforcement Learning. [`arxiv`](https://arxiv.org/abs/1703.00956) :star: 10 | - Boosting the Actor with Dual Critic. [`arxiv`](https://arxiv.org/abs/1712.10282) 11 | - Bridging the Gap Between Value and Policy Based Reinforcement Learning. [`arxiv`](https://arxiv.org/abs/1702.08892) 12 | - Car Racing using Reinforcement Learning. [`pdf`](https://web.stanford.edu/class/cs221/2017/restricted/p-final/elibol/final.pdf) 13 | - Cold-Start Reinforcement Learning with Softmax Policy Gradients. [`arxiv`](https://arxiv.org/abs/1709.09346) 14 | - Curiosity-driven Exploration by Self-supervised Prediction. [`arxiv`](https://arxiv.org/abs/1705.05363) [`tensorflow`](https://github.com/pathak22/noreward-rl) 15 | - Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning. [`arxiv`](https://arxiv.org/abs/1712.06567) [`code`](https://github.com/uber-common/deep-neuroevolution) 16 | - DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning. [`arxiv`](https://arxiv.org/abs/1707.06690) [`code`](https://github.com/xwhan/DeepPath) 17 | - Deep Reinforcement Learning: An Overview. [`arxiv`](https://arxiv.org/abs/1701.07274) 18 | - Deep Reinforcement Learning for Unsupervised Video Summarization with Diversity-Representativeness Reward. [`arxiv`](https://arxiv.org/abs/1801.00054) [`code`](https://github.com//KaiyangZhou/vsumm-reinforce) 19 | - Deep reinforcement learning from human preferences. [`arxiv`](https://arxiv.org/abs/1706.03741) 20 | - Deep Reinforcement Learning that Matters. [`arxiv`](https://arxiv.org/abs/1709.06560) [`code`](https://github.com/Breakend/DeepReinforcementLearningThatMatters) 21 | - Device Placement Optimization with Reinforcement Learning. [`arxiv`](https://arxiv.org/abs/1706.04972) 22 | - Distributional Reinforcement Learning with Quantile Regression. [`arxiv`](https://arxiv.org/abs/1710.10044) 23 | - End-to-End Optimization of Task-Oriented Dialogue Model with Deep Reinforcement Learning. [`arxiv`](https://arxiv.org/abs/1711.10712) 24 | - Evolution Strategies as a Scalable Alternative to Reinforcement Learning. [`arxiv`](https://arxiv.org/abs/1703.03864) 25 | - Feature Control as Intrinsic Motivation for Hierarchical Reinforcement Learning. [`arxiv`](https://arxiv.org/abs/1705.06769) 26 | - Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations. [`arxiv`](https://arxiv.org/abs/1709.10087) 27 | - Learning how to Active Learn: A Deep Reinforcement Learning Approach. [`arxiv`](https://arxiv.org/abs/1708.02383) [`tensorflow`](https://github.com/mengf1/PAL) 28 | - Learning Multimodal Transition Dynamics for Model-Based Reinforcement Learning. [`arxiv`](https://arxiv.org/abs/1705.00470) [`tensorflow`](https://github.com/tmoer/multimodal_varinf) 29 | - MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective Intelligence. [`arxiv`](https://arxiv.org/abs/1712.00600) [`code`](https://github.com//geek-ai/MAgent) :star: 30 | - Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm. [`arxiv`](https://arxiv.org/abs/1712.01815) 31 | - Micro-Objective Learning : Accelerating Deep Reinforcement Learning through the Discovery of Continuous Subgoals. [`arxiv`](https://arxiv.org/abs/1703.03933) 32 | - Neural Architecture Search with Reinforcement Learning. [`arxiv`](Neural Architecture Search with Reinforcement Learning) [`tensorflow`](https://github.com/tensorflow/models) 33 | - Neural Map: Structured Memory for Deep Reinforcement Learning. [`arxiv`](https://arxiv.org/abs/1702.08360) 34 | - Observational Learning by Reinforcement Learning. [`arxiv`](https://arxiv.org/abs/1706.06617) 35 | - Overcoming Exploration in Reinforcement Learning with Demonstrations. [`arxiv`](https://arxiv.org/abs/1709.10089) 36 | - Practical Network Blocks Design with Q-Learning. [`arxiv`](https://arxiv.org/abs/1708.05552) 37 | - Rainbow: Combining Improvements in Deep Reinforcement Learning. [`arxiv`](https://arxiv.org/abs/1710.02298) 38 | - Reinforcement Learning for Architecture Search by Network Transformation. [`arxiv`](https://arxiv.org/abs/1707.04873) [`code`](https://github.com/han-cai/RL4AS_NetTrans) 39 | - Reinforcement Learning via Recurrent Convolutional Neural Networks. [`arxiv`](https://arxiv.org/abs/1701.02392) [`code`](https://github.com/tanmayshankar/RCNN_MDP) 40 | - Reinforcement Learning with a Corrupted Reward Channel. [`arxiv`](https://arxiv.org/abs/1705.08417) :star: 41 | - Reinforcement Learning with Deep Energy-Based Policies. [`arxiv`](https://arxiv.org/abs/1702.08165) [`code`](https://github.com/haarnoja/softqlearning) 42 | - Reinforcement Learning with External Knowledge and Two-Stage Q-functions for Predicting Popular Reddit Threads. [`arxiv`](https://arxiv.org/abs/1704.06217) 43 | - Robust Deep Reinforcement Learning with Adversarial Attacks. [`arxiv`](https://arxiv.org/abs/1712.03632) 44 | - Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning. [`arxiv`](https://arxiv.org/abs/1709.00103) 45 | - Shallow Updates for Deep Reinforcement Learning. [`arxiv`](https://arxiv.org/abs/1705.07461) [`code`](https://github.com/Shallow-Updates-for-Deep-RL/Shallow_Updates_for_Deep_RL) 46 | - Stochastic Neural Networks for Hierarchical Reinforcement Learning. [`pdf`](https://openreview.net/pdf?id=B1oK8aoxe) [`code`](https://github.com/florensacc/snn4hrl) 47 | - Tackling Error Propagation through Reinforcement Learning: A Case of Greedy Dependency Parsing. [`arxiv`](https://arxiv.org/abs/1702.06794) [`code`](https://bitbucket.org/cltl/redep-java) 48 | - Task-Oriented Query Reformulation with Reinforcement Learning. [`arxiv`](https://arxiv.org/abs/1704.04572) [`code`](https://github.com/nyu-dl/QueryReformulator) 49 | - Teaching a Machine to Read Maps with Deep Reinforcement Learning. [`arxiv`](https://arxiv.org/abs/1711.07479) [`code`](https://github.com//OliverRichter/map-reader) 50 | - TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning. [`arxiv`](https://arxiv.org/abs/1710.11417) [`code`](https://github.com/oxwhirl/treeqn/) 51 | - Value Prediction Network. [`arxiv`](https://arxiv.org/abs/1707.03497) 52 | - Variational Deep Q Network. [`arxiv`](https://arxiv.org/abs/1711.11225) 53 | - Virtual-to-real Deep Reinforcement Learning: Continuous Control of Mobile Robots for Mapless Navigation.[`arxiv`](https://arxiv.org/abs/1703.00420) 54 | - Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning. [`arxiv`](https://arxiv.org/abs/1706.05064) 55 | -------------------------------------------------------------------------------- /papers/2018/cv.md: -------------------------------------------------------------------------------- 1 | # Computer Vision 2 | 3 | - 3D CNN-based classification using sMRI and MD-DTI images for Alzheimer disease studies. [`arxiv`](https://arxiv.org/abs/1801.05968) 4 | - A Closed-form Solution to Photorealistic Image Stylization. [`arxiv`](https://arxiv.org/abs/1802.06474) [`code`](https://github.com/NVIDIA/FastPhotoStyle) :star: 5 | - A Convolutional Autoencoder Approach to Learn Volumetric Shape Representations for Brain Structures. [`arxiv`](https://arxiv.org/abs/1810.07746) 6 | - Adversarial Learning for Semi-Supervised Semantic Segmentation. [`arxiv`](https://arxiv.org/abs/1802.07934) [`pytorch`](https://github.com/hfslyc/AdvSemiSeg) 7 | - Adversarial Training of Variational Auto-encoders for High Fidelity Image Generation. [`arxiv`](https://arxiv.org/abs/1804.10323) 8 | - Attention-Aware Compositional Network for Person Re-identification. [`arxiv`](https://arxiv.org/abs/1805.03344) 9 | - Attention U-Net: Learning Where to Look for the Pancreas. [`arxiv`](https://arxiv.org/abs/1804.03999) [`code`](https://github.com/ozan-oktay/Attention-Gated-Networks) 10 | - AutoAugment: Learning Augmentation Policies from Data. [`arxiv`](https://arxiv.org/abs/1805.09501) 11 | - Avatar-Net: Multi-scale Zero-shot Style Transfer by Feature Decoration. [`arxiv`](https://arxiv.org/abs/1805.03857) 12 | - Choose Your Neuron: Incorporating Domain Knowledge through Neuron-Importance. [`arxiv`](https://arxiv.org/abs/1808.02861) 13 | - Compressed Sensing with Deep Image Prior and Learned Regularization. [`arxiv`](https://arxiv.org/abs/1806.06438) 14 | - Compressed Video Action Recognition. [`arxiv`](https://arxiv.org/abs/1712.00636) [`code`](https://github.com/chaoyuaw/pytorch-coviar) :star: 15 | - Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images. [`arxiv`](https://arxiv.org/abs/1804.04488) 16 | - Deep Clustering for Unsupervised Learning of Visual Features. [`arxiv`](https://arxiv.org/abs/1807.05520) 17 | - Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis. [`arxiv`](https://arxiv.org/abs/1802.00752) [`code`](https://github.com/alexander-rakhlin/ICIAR2018) 18 | - DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images. [`arxiv`](https://arxiv.org/abs/1805.06561) 19 | - Deep Lip Reading: a comparison of models and an online application. [`arxiv`](https://arxiv.org/abs/1806.06053) 20 | - Deep Pose Consensus Networks. [`arxiv`](https://arxiv.org/abs/1803.08190) 21 | - Depth CNNs for RGB-D scene recognition: learning from scratch better than transferring from RGB-CNNs. [`arxiv`](https://arxiv.org/abs/1801.06797) [`code`](https://github.com/songxinhang/D-CNN) 22 | - Detecting Visual Relationships Using Box Attention. [`arxiv`](https://arxiv.org/abs/1807.02136) 23 | - Detection and Segmentation of Manufacturing Defects with Convolutional Neural Networks and Transfer Learning. [`arxiv`](https://arxiv.org/abs/1808.02518) 24 | - DynSLAM: Tracking, Mapping and Inpainting in Dynamic Scenes. [`arxiv`](https://arxiv.org/abs/1806.05620) 25 | - Encoding, Fast and Slow: Low-Latency Video Processing Using Thousands of Tiny Threads. [`arxiv`](https://www.usenix.org/system/files/conference/nsdi17/nsdi17-fouladi.pdf) 26 | - End-to-End Saliency Mapping via Probability Distribution Prediction. [`arxiv`](https://arxiv.org/abs/1804.01793) 27 | - Fast Semantic Segmentation on Video Using Motion Vector-Based Feature Interpolation. [`arxiv`](https://arxiv.org/abs/1803.07742) 28 | - From Selective Deep Convolutional Features to Compact Binary Representations for Image Retrieval. [`arxiv`](https://arxiv.org/abs/1802.02899) [`code`](https://github.com/hnanhtuan/selectiveConvFeature) 29 | - Geometrical Stem Detection from Image Data for Precision Agriculture. [`arxiv`](https://arxiv.org/abs/1812.05415) [`code`](https://github.com/PRBonn/geometrical_stem_detection) 30 | - Harmonious Attention Network for Person Re-Identification. [`arxiv`](https://arxiv.org/abs/1802.08122) 31 | - IGCV2: Interleaved Structured Sparse Convolutional Neural Networks. [`arxiv`](https://arxiv.org/abs/1804.06202) 32 | - Image Inpainting for Irregular Holes Using Partial Convolutions. [`arxiv`](https://arxiv.org/abs/1804.07723) 33 | - Image-to-image translation for cross-domain disentanglement. [`arxiv`](https://arxiv.org/abs/1805.09730) 34 | - Image Transformer. [`arxiv`](https://arxiv.org/abs/1802.05751) :star: 35 | - Infrared and Visible Image Fusion using a Deep Learning Framework. [`arxiv`](https://arxiv.org/abs/1804.06992) [`code`](https://github.com/exceptionLi/imagefusion_deeplearning) 36 | - Instance-level Human Parsing via Part Grouping Network. [`arxiv`](https://arxiv.org/abs/1808.00157) 37 | - LF-Net: Learning Local Features from Images. [`arxiv`](https://arxiv.org/abs/1805.09662) 38 | - Low-Shot Learning from Imaginary Data. [`arxiv`](https://arxiv.org/abs/1801.05401) 39 | - Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes. [`arxiv`](https://arxiv.org/abs/1807.02242) 40 | - MegaDepth: Learning Single-View Depth Prediction from Internet Photos. [`arxiv`](https://arxiv.org/abs/1804.00607) [`code`](https://github.com/lixx2938/MegaDepth) 41 | - ModaNet: A Large-Scale Street Fashion Dataset with Polygon Annotations. [`arxiv`](https://arxiv.org/abs/1807.01394) 42 | - Multimodal Unsupervised Image-to-Image Translation. [`arxiv`](https://arxiv.org/abs/1804.04732) [`code`](https://github.com/nvlabs/MUNIT) :star: 43 | - Multitask Learning on Graph Neural Networks - Learning Multiple Graph Centrality Measures with a Unified Network. [`arxiv`](https://arxiv.org/abs/1809.07695) 44 | - Multi-view Consistency as Supervisory Signal for Learning Shape and Pose Prediction. [`arxiv`](https://arxiv.org/abs/1801.03910) [`code`](https://github.com//shubhtuls/mvcSnP) 45 | - PCN: Point Completion Network. [`arxiv`](https://arxiv.org/abs/1808.00671) [`code`](https://github.com/TonythePlaneswalker/pcn) 46 | - Pixel-wise Attentional Gating for Parsimonious Pixel Labeling. [`arxiv`](https://arxiv.org/abs/1805.01556) [`code`](https://github.com/aimerykong/Pixel-Attentional-Gating) 47 | - PlaneNet: Piece-wise Planar Reconstruction from a Single RGB Image. [`pdf`](https://eng.ucmerced.edu/people/jyang44/papers/cvpr2018_PlaneNet_camera_ready.pdf) [`code`](https://github.com/art-programmer/PlaneNet) 48 | - PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place Recognition. [`arxiv`](https://arxiv.org/abs/1804.03492) [`code`](https://github.com/mikacuy/pointnetvlad) 49 | - Quantizing deep convolutional networks for efficient inference: A whitepaper. [`arxiv`](https://arxiv.org/abs/1806.08342) 50 | - Recurrent Squeeze-and-Excitation Context Aggregation Net for Single Image Deraining. [`arxiv`](https://arxiv.org/abs/1807.05698) [`pytorch`](https://github.com/XiaLiPKU/RESCAN) 51 | - ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design. [`arxiv`](https://arxiv.org/abs/1807.11164) [`code`](https://github.com/Randl/ShuffleNetV2-pytorch) 52 | - Simple Baselines for Human Pose Estimation and Tracking. [`arxiv`](https://arxiv.org/abs/1804.06208) 53 | - Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition. [`arxiv`](https://arxiv.org/abs/1801.07455) [`code`](https://github.com/yysijie/st-gcn) 54 | - Stacked U-Nets: A No-Frills Approach to Natural Image Segmentation. [`arxiv`](https://arxiv.org/abs/1804.10343) [`code`](https://github.com/shahsohil/sunets) 55 | - Text to Image Synthesis Using Generative Adversarial Networks. [`arxiv`](https://arxiv.org/abs/1805.00676) 56 | - Total Capture: A 3D Deformation Model for Tracking Faces, Hands, and Bodies. [`pdf`](http://openaccess.thecvf.com/content_cvpr_2018/papers/Joo_Total_Capture_A_CVPR_2018_paper.pdf) 57 | - Towards Image Understanding from Deep Compression without Decoding. [`arxiv`](https://arxiv.org/abs/1803.06131) 58 | - Towards Semantic SLAM: Points, Planes and Objects. [`arxiv`](https://arxiv.org/abs/1804.09111) 59 | - Trajectory Factory: Tracklet Cleaving and Re-connection by Deep Siamese Bi-GRU for Multiple Object Tracking. [`arxiv`](https://arxiv.org/abs/1804.04555) 60 | - Unsupervised Discovery of Object Landmarks as Structural Representations. [`arxiv`](https://arxiv.org/abs/1804.04412) 61 | - Unsupervised Training for 3D Morphable Model Regression. [`arxiv`](https://arxiv.org/abs/1806.06098) 62 | - Video Person Re-identification by Temporal Residual Learning. [`arxiv`](https://arxiv.org/abs/1802.07918) 63 | 64 | ## Face Recognition 65 | 66 | - Additive Margin Softmax for Face Verification. [`arxiv`](https://arxiv.org/abs/1801.05599) [`code`](https://github.com/happynear/AMSoftmax) :star: 67 | - Anchor Cascade for Efficient Face Detection. [`arxiv`](https://arxiv.org/abs/1805.03363) 68 | - Detecting and counting tiny faces. [`arxiv`](https://arxiv.org/abs/1801.06504) [`code`](https://github.com/alexattia/ExtendedTinyFaces) 69 | - Face Recognition: From Traditional to Deep Learning Methods. [`arxiv`](https://arxiv.org/abs/1811.00116) 70 | - RED-Net: A Recurrent Encoder-Decoder Network for Video-based Face Alignment. [`arxiv`](https://arxiv.org/abs/1801.06066) 71 | - SSR-Net: A Compact Soft Stagewise Regression Network for Age Estimation. [`pdf`](https://github.com/shamangary/SSR-Net/blob/master/ijcai18_ssrnet_pdfa_2b.pdf) [`code`](https://github.com/shamangary/SSR-Net) 72 | - Survey of Face Detection on Low-quality Images. [`arxiv`](https://arxiv.org/abs/1804.07362) 73 | 74 | ## Image Classifier 75 | 76 | - Bag of Tricks for Image Classification with Convolutional Neural Networks. [`arxiv`](https://arxiv.org/abs/1812.01187) 77 | - Combined convolutional and recurrent neural networks for hierarchical classification of images. [`arxiv`](https://arxiv.org/abs/1809.09574) 78 | - Global Weighted Average Pooling Bridges Pixel-level Localization and Image-level Classification. [`arxiv`](https://arxiv.org/abs/1809.08264) 79 | - Regularized Evolution for Image Classifier Architecture Search. [`arxiv`](https://arxiv.org/abs/1802.01548) 80 | - Rethinking Feature Distribution for Loss Functions in Image Classification. [`arxiv`](https://arxiv.org/abs/1803.02988) [`code`](https://github.com/WeitaoVan/L-GM-loss) 81 | 82 | ## Image Generate 83 | 84 | - Enhancing Underwater Imagery using Generative Adversarial Networks. [`arxiv`](https://arxiv.org/abs/1801.04011) [`code`](https://github.com//cameronfabbri/Underwater-Color-Correction) 85 | - ST-GAN: Spatial Transformer Generative Adversarial Networks for Image Compositing. [`arxiv`](https://arxiv.org/abs/1803.01837) [`code`](https://github.com/chenhsuanlin/spatial-transformer-GAN) 86 | 87 | 88 | ## Object Detection 89 | 90 | - A Generalized Active Learning Approach for Unsupervised Anomaly Detection. [`arxiv`](https://arxiv.org/abs/1805.09411) 91 | - CornerNet: Detecting Objects as Paired Keypoints. [`arxiv`](https://arxiv.org/abs/1808.01244) [`code`](https://github.com/umich-vl/CornerNet) 92 | - Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation. [`arxiv`](https://arxiv.org/abs/1803.11365) [`code`](https://github.com/naoto0804/cross-domain-detection) 93 | - CubeSLAM: Monocular 3D Object Detection and SLAM without Prior Models. [`arxiv`](https://arxiv.org/abs/1806.00557) 94 | - Deep Learning Approach for Very Similar Objects Recognition Application on Chihuahua and Muffin Problem. [`arxiv`](https://arxiv.org/abs/1801.09573) 95 | - Deep Learning for Generic Object Detection: A Survey. [`arxiv`](https://arxiv.org/abs/1809.02165) 96 | - Dense Object Nets: Learning Dense Visual Object Descriptors By and For Robotic Manipulation. [`arxiv`](https://arxiv.org/abs/1806.08756) [`code`](https://github.com/RobotLocomotion/pytorch-dense-correspondence) 97 | - Detect-and-Track: Efficient Pose Estimation in Videos. [`arxiv`](https://arxiv.org/abs/1712.09184) [`code`](https://github.com/facebookresearch/DetectAndTrack) :star: 98 | - DetNet: A Backbone network for Object Detection. [`arxiv`](https://arxiv.org/abs/1804.06215) 99 | - Domain Adaptive Faster R-CNN for Object Detection in the Wild. [`arxiv`](https://arxiv.org/abs/1803.03243) [`code`](https://github.com/yuhuayc/da-faster-rcnn) 100 | - Faster RER-CNN: application to the detection of vehicles in aerial images. [`arxiv`](https://arxiv.org/abs/1809.07628) 101 | - Faster Training of Mask R-CNN by Focusing on Instance Boundaries. [`arxiv`](https://arxiv.org/abs/1809.07069) 102 | - GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training. [`arxiv`](https://arxiv.org/abs/1805.06725) [`code`](https://github.com/samet-akcay/ganomaly) 103 | - Multi-Resolution 3D Convolutional Neural Networks for Object Recognition. [`arxiv`](https://arxiv.org/abs/1805.12254) 104 | - Object Detection for Comics using Manga109 Annotations. [`arxiv`](http://xxx.itp.ac.cn/abs/1803.08670) 105 | - Object Detection from Scratch with Deep Supervision. [`arxiv`](https://arxiv.org/abs/1809.09294) 106 | - Object Detection in Satellite Imagery using 2-Step Convolutional Neural Networks. [`arxiv`](https://arxiv.org/abs/1808.02996) 107 | - Optimizing Video Object Detection via a Scale-Time Lattice. [`url`](http://mmlab.ie.cuhk.edu.hk/projects/ST-Lattice/) [`code`](https://github.com/hellock/scale-time-lattice) 108 | - Pseudo Mask Augmented Object Detection. [`arxiv`](https://arxiv.org/abs/1803.05858) 109 | - Probabilistic Model of Object Detection Based on Convolutional Neural Network. [`arxiv`](https://arxiv.org/abs/1808.08272) 110 | - Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks. [`arxiv`](https://arxiv.org/abs/1809.03193) 111 | - Relation Networks for Object Detection. [`arxiv`](https://arxiv.org/abs/1711.11575) [`code`](https://github.com/msracver/Relation-Networks-for-Object-Detection) 112 | - Road Damage Detection Using Deep Neural Networks with Images Captured Through a Smartphone. [`arxiv`](https://arxiv.org/abs/1801.09454) [`code`](https://github.com/sekilab/RoadDamageDetector/) 113 | - Single-Shot Bidirectional Pyramid Networks for High-Quality Object Detection. [`arxiv`](https://arxiv.org/abs/1803.08208) 114 | - SNIPER: Efficient Multi-Scale Training. [`arxiv`](https://arxiv.org/abs/1805.09300) [`code`](https://github.com/mahyarnajibi/SNIPER) 115 | - Subpixel-Precise Tracking of Rigid Objects in Real-time. [`arxiv`](https://arxiv.org/abs/1807.01952) 116 | - YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud. [`arxiv`](https://arxiv.org/abs/1808.02350) 117 | - You Only Look Twice: Rapid Multi-Scale Object Detection In Satellite Imagery. [`arxiv`](https://arxiv.org/abs/1805.09512) [`code`](https://github.com/CosmiQ/yolt) 118 | - Zero-Shot Object Detection by Hybrid Region Embedding. [`arxiv`](https://arxiv.org/abs/1805.06157) 119 | - Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts. [`arxiv`](https://arxiv.org/abs/1803.06049) 120 | 121 | ## Object Segmentation 122 | 123 | - An application of cascaded 3D fully convolutional networks for medical image segmentation. [`arxiv`](https://arxiv.org/abs/1803.05431) [`code`](https://github.com/holgerroth/3Dunet_abdomen_cascade) 124 | - An Iterative Boundary Random Walks Algorithm for Interactive Image Segmentation. [`arxiv`](https://arxiv.org/abs/1808.03002) 125 | - A Probabilistic U-Net for Segmentation of Ambiguous Images. [`arxiv`](https://arxiv.org/abs/1806.05034) 126 | - A two-stage 3D Unet framework for multi-class segmentation on full resolution image. [`arxiv`](https://arxiv.org/abs/1804.04341) 127 | - ClusterNet: Instance Segmentation in RGB-D Images. [`arxiv`](https://arxiv.org/abs/1807.08894) 128 | - Dark Model Adaptation: Semantic Image Segmentation from Daytime to Nighttime. [`arxiv`](https://arxiv.org/abs/1810.02575) 129 | - Deep Learning for Semantic Segmentation on Minimal Hardware. [`arxiv`](https://arxiv.org/abs/1807.05597) 130 | - Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training. [`arxiv`](https://arxiv.org/abs/1810.07911) [`code`](https://github.com/yzou2/cbst) 131 | - Fast and Accurate Online Video Object Segmentation via Tracking Parts. [`arxiv`](https://arxiv.org/abs/1806.02323) [`code`](https://github.com/JingchunCheng/FAVOS) 132 | - Learning to Segment Every Thing. [`pdf`](http://openaccess.thecvf.com/content_cvpr_2018/papers/Hu_Learning_to_Segment_CVPR_2018_paper.pdf) [`code`](https://github.com/skrish13/PyTorch-mask-x-rcnn) :star: 133 | - Learning to Segment Medical Images with Scribble-Supervision Alone. [`arxiv`](https://arxiv.org/abs/1807.04668) 134 | - MaskFusion: Real-Time Recognition, Tracking and Reconstruction of Multiple Moving Objects. [`arxiv`](https://arxiv.org/abs/1804.09194) 135 | - NeuroNet: Fast and Robust Reproduction of Multiple Brain Image Segmentation Pipelines. [`arxiv`](https://arxiv.org/abs/1806.04224) 136 | - Panoptic Segmentation with a Joint Semantic and Instance Segmentation Network. [`arxiv`](https://arxiv.org/abs/1809.02110) 137 | - Pathology Segmentation using Distributional Differences to Images of Healthy Origin. [`arxiv`](https://arxiv.org/abs/1805.10344) 138 | - PersonLab: Person Pose Estimation and Instance Segmentation with a Bottom-Up, Part-Based, Geometric Embedding Model. [`arxiv`](https://arxiv.org/abs/1803.08225) 139 | - Pixel Level Data Augmentation for Semantic Image Segmentation using Generative Adversarial Networks. [`arxiv`](https://arxiv.org/abs/1811.00174) 140 | - PointSIFT: A SIFT-like Network Module for 3D Point Cloud Semantic Segmentation. [`arxiv`](https://arxiv.org/abs/1807.00652) [`code`](https://github.com/MVIG-SJTU/pointSIFT) 141 | - Pyramid Attention Network for Semantic Segmentation. [`arxiv`](https://arxiv.org/abs/1805.10180) 142 | - Semantic Binary Segmentation using Convolutional Networks without Decoders. [`arxiv`](https://arxiv.org/abs/1805.00138) 143 | - Y-Net: Joint Segmentation and Classification for Diagnosis of Breast Biopsy Images. [`arxiv`](https://arxiv.org/abs/1806.01313) 144 | 145 | ### OCR 146 | 147 | - Fooling OCR Systems with Adversarial Text Images. [`arxiv`](https://arxiv.org/abs/1802.05385) 148 | - Open Set Chinese Character Recognition using Multi-typed Attributes. [`arxiv`](https://arxiv.org/abs/1808.08993) 149 | -------------------------------------------------------------------------------- /papers/2018/nlp.md: -------------------------------------------------------------------------------- 1 | ## Nature language process 2 | 3 | - 500+ Times Faster Than Deep Learning (A Case Study Exploring Faster Methods for Text Mining StackOverflow). [`arxiv`](https://arxiv.org/abs/1802.05319) 4 | - A Deep Ensemble Model with Slot Alignment for Sequence-to-Sequence Natural Language Generation. [`arxiv`](https://arxiv.org/abs/1805.06553) 5 | - Analysis Methods in Neural Language Processing: A Survey. [`arxiv`](https://arxiv.org/abs/1812.08951) 6 | - An end-to-end TextSpotter with Explicit Alignment and Attention. [`arxiv`](https://arxiv.org/abs/1803.03474) [`code`](https://github.com/tonghe90/textspotter) 7 | - An Introductory Survey on Attention Mechanisms in NLP Problems. [`arxiv`](https://arxiv.org/abs/1811.05544) 8 | - Annotation Artifacts in Natural Language Inference Data. [`arxiv`](https://arxiv.org/abs/1803.02324) :star: 9 | - A Universal Music Translation Network. [`arxiv`](https://arxiv.org/abs/1805.07848) 10 | - Building Dynamic Knowledge Graphs from Text using Machine Reading Comprehension. [`arxiv`](https://arxiv.org/abs/1810.05682) 11 | - Calculating the similarity between words and sentences using a lexical database and corpus statistics. [`arxiv`](https://arxiv.org/abs/1802.05667) 12 | - Chinese Text in the Wild. [`arxiv`](https://arxiv.org/abs/1803.00085) 13 | - Content Selection in Deep Learning Models of Summarization. [`arxiv`](https://arxiv.org/abs/1810.12343) 14 | - Constituency Parsing with a Self-Attentive Encoder. [`arxiv`](https://arxiv.org/abs/1805.01052) [`code`](https://github.com/nikitakit/self-attentive-parser) 15 | - Embedding Logical Queries on Knowledge Graphs. [`arxiv`](https://arxiv.org/abs/1806.01445) [`code`](https://github.com/williamleif/graphqembed) 16 | - Extracting Action Sequences from Texts Based on Deep Reinforcement Learning. [`arxiv`](https://arxiv.org/abs/1803.02632) 17 | - Generating Wikipedia by Summarizing Long Sequences. [`arxiv`](https://arxiv.org/abs/1801.10198) :star: 18 | - How Images Inspire Poems: Generating Classical Chinese Poetry from Images with Memory Networks. [`arxiv`](https://arxiv.org/abs/1803.02994) 19 | - IncepText: A New Inception-Text Module with Deformable PSROI Pooling for Multi-Oriented Scene Text Detection. [`arxiv`](https://arxiv.org/abs/1805.01167) 20 | - Linguistically-Informed Self-Attention for Semantic Role Labeling. [`arxiv`](https://arxiv.org/abs/1804.08199) :star: 21 | - Neural Relational Inference for Interacting Systems. [`arxiv`](https://arxiv.org/abs/1802.04687) [`code`](https://github.com/ethanfetaya/nri) 22 | - Neural Text Generation: Past, Present and Beyond. [`arxiv`](https://arxiv.org/abs/1803.07133) 23 | - PixelLink: Detecting Scene Text via Instance Segmentation. [`arxiv`](https://arxiv.org/abs/1801.01315) [`code`](https://github.com/ZJULearning/pixel_link) 24 | - Shape Robust Text Detection with Progressive Scale Expansion Network. [`arxiv`](https://arxiv.org/abs/1806.02559) [`code`](https://github.com/whai362/PSENet) 25 | - Text2Shape: Generating Shapes from Natural Language by Learning Joint Embeddings. [`arxiv`](https://arxiv.org/abs/1803.08495) 26 | - TextTopicNet - Self-Supervised Learning of Visual Features Through Embedding Images on Semantic Text Spaces. [`arxiv`](https://arxiv.org/abs/1807.02110) [`code`](https://github.com/lluisgomez/TextTopicNet) 27 | - Using J-K fold Cross Validation to Reduce Variance When Tuning NLP Models. [`arxiv`](https://arxiv.org/abs/1806.07139) [`code`](https://github.com/henrymoss/COLING2018) 28 | 29 | ### Chatbot 30 | 31 | - A Deep Reinforcement Learning Chatbot (Short Version). [`arxiv`](https://arxiv.org/abs/1801.06700) 32 | - Dialogue Generation: From Imitation Learning to Inverse Reinforcement Learning. [`arxiv`](https://arxiv.org/abs/1812.03509) 33 | - Goal-Oriented Chatbot Dialog Management Bootstrapping with Transfer Learning. [`arxiv`](https://arxiv.org/abs/1802.00500) 34 | - Mem2Seq: Effectively Incorporating Knowledge Bases into End-to-End Task-Oriented Dialog Systems. [`arxiv`](https://arxiv.org/abs/1804.08217) [`code`](https://github.com/HLTCHKUST/Mem2Seq) 35 | - The Design and Implementation of XiaoIce, an Empathetic Social Chatbot. [`arxiv`](https://arxiv.org/abs/1812.08989) 36 | 37 | ### Embeddings 38 | 39 | - An efficient framework for learning sentence representations. [`arxiv`](https://arxiv.org/abs/1803.02893) 40 | - A Hierarchical Multi-task Approach for Learning Embeddings from Semantic Tasks. [`arxiv`](https://arxiv.org/abs/1811.06031) [`code`](https://github.com/huggingface/hmtl) 41 | - A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings. [`arxiv`](https://arxiv.org/abs/1805.06297) [`code`](https://github.com/artetxem/vecmap) 42 | - Beyond Word Embeddings: Learning Entity and Concept Representations from Large Scale Knowledge Bases. [`arxiv`](https://arxiv.org/abs/1801.00388) 43 | - Concept2vec: Metrics for Evaluating Quality of Embeddings for Ontological Concepts. [`arxiv`](https://arxiv.org/abs/1803.04488) 44 | - Deep contextualized word representations. [`arxiv`](https://arxiv.org/abs/1802.05365) 45 | - Evaluating Compositionality in Sentence Embeddings. [`arxiv`](https://arxiv.org/abs/1802.04302) [`code`](https://github.com/ishita-dg/ScrambleTests) 46 | - From Word to Sense Embeddings: A Survey on Vector Representations of Meaning. [`arxiv`](https://arxiv.org/abs/1805.04032) 47 | - Learning Domain-Sensitive and Sentiment-Aware Word Embeddings. [`arxiv`](https://arxiv.org/abs/1805.03801) 48 | - Learning Role-based Graph Embeddings. [`arxiv`](https://arxiv.org/abs/1802.02896) [`code`](https://github.com/benedekrozemberczki/role2vec) 49 | - Learning Word Embeddings for Low-resource Languages by PU Learning. [`arxiv`](https://arxiv.org/abs/1805.03366) 50 | - On the Dimensionality of Word Embedding. [`arxiv`](https://arxiv.org/abs/1812.04224) [`code`](https://github.com/ziyin-dl/word-embedding-dimensionality-selection) 51 | - Poincaré GloVe: Hyperbolic Word Embeddings. [`arxiv`](https://arxiv.org/abs/1810.06546) 52 | - Query2Vec: NLP Meets Databases for Generalized Workload Analytics. [`arxiv`](https://arxiv.org/abs/1801.05613) 53 | - Semantic projection: recovering human knowledge of multiple, distinct object features from word embeddings. [`arxiv`](https://arxiv.org/abs/1802.01241) 54 | - Speech2Vec: A Sequence-to-Sequence Framework for Learning Word Embeddings from Speech. [`arxiv`](https://arxiv.org/abs/1803.08976) 55 | - Word Embedding Attention Network: Generating Words by Querying Distributed Word Representations for Paraphrase Generation. [`arxiv`](https://arxiv.org/abs/1803.01465) [`code`](https://github.com/lancopku/WEAN) 56 | - Word2Bits - Quantized Word Vectors. [`arxiv`](https://arxiv.org/abs/1803.05651) [`code`](https://github.com/agnusmaximus/Word2Bits) :star: 57 | 58 | ### Keyphrase Extraction 59 | 60 | - EmbedRank: Unsupervised Keyphrase Extraction using Sentence Embeddings. [`arxiv`](https://arxiv.org/abs/1801.04470) 61 | 62 | ### Knowledge Graphs 63 | 64 | - Variational Knowledge Graph Reasoning. [`arxiv`](https://arxiv.org/abs/1803.06581) 65 | 66 | ### Language Model 67 | 68 | - An Analysis of Neural Language Modeling at Multiple Scales. [`arxiv`](https://arxiv.org/abs/1803.08240) [`code`](https://github.com/salesforce/awd-lstm-lm) 69 | 70 | ### Pos-tagging 71 | 72 | - Evaluating Layers of Representation in Neural Machine Translation on Part-of-Speech and Semantic Tagging Tasks. [`arxiv`](https://arxiv.org/abs/1801.07772) [`code`](https://github.com/boknilev/nmt-repr-analysis) 73 | 74 | ### QA 75 | 76 | - A Corpus for Modeling Word Importance in Spoken Dialogue Transcripts. [`arxiv`](https://arxiv.org/abs/1801.09746) 77 | - A Question-Focused Multi-Factor Attention Network for Question Answering. [`arxiv`](https://arxiv.org/abs/1801.08290) [`code`](https://github.com/nusnlp/amanda) 78 | - Being curious about the answers to questions: novelty search with learned attention. [`arxiv`](https://arxiv.org/abs/1806.00201) [`code`](https://github.com/arayabrain/QuestionDrivenNovelty) 79 | - Bilinear Attention Networks. [`arxiv`](https://arxiv.org/abs/1805.07932) [`code`](https://github.com/jnhwkim/ban-vqa) 80 | - EARL: Joint Entity and Relation Linking for Question Answering over Knowledge Graphs. [`arxiv`](https://arxiv.org/abs/1801.03825) [`code`](https://github.com//AskNowQA/EARL) 81 | - Finding ReMO (Related Memory Object): A Simple Neural Architecture for Text based Reasoning. [`arxiv`](https://arxiv.org/abs/1801.08459) [`code`](https://github.com/juung/RMN) 82 | - The Natural Language Decathlon: Multitask Learning as Question Answering. [`arxiv`](https://arxiv.org/abs/1806.08730) 83 | - Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge. [`arxiv`](https://arxiv.org/abs/1803.05457) [`code`](https://github.com/allenai/arc-solvers) 84 | - TVQA: Localized, Compositional Video Question Answering. [`arxiv`](https://arxiv.org/abs/1809.01696) 85 | - Visual Question Generation as Dual Task of Visual Question Answering. [`url`](http://cvboy.com/publication/cvpr2018_iqan/) [`code`](https://github.com/yikang-li/iQAN) 86 | 87 | ### NER 88 | 89 | - Chinese NER Using Lattice LSTM. [`arxiv`](https://arxiv.org/pdf/1805.02023.pdf) [`code`](https://github.com/jiesutd/LatticeLSTM) 90 | - Named Entity Disambiguation using Deep Learning on Graphs. [`arxiv`](https://arxiv.org/abs/1810.09164) [`code`](https://github.com/contextscout/ned-graphs) 91 | 92 | ### NMT 93 | 94 | - Achieving Human Parity on Automatic Chinese to English News Translation. [`arxiv`](https://arxiv.org/abs/1803.05567) 95 | - Apply Chinese Radicals Into Neural Machine Translation: Deeper Than Character Level. [`arxiv`](https://arxiv.org/abs/1805.01565) 96 | - Phrase-Based & Neural Unsupervised Machine Translation. [`arxiv`](https://arxiv.org/abs/1804.07755) 97 | - Self-Attention with Relative Position Representations. [`arxiv`](https://arxiv.org/abs/1803.02155) 98 | 99 | 100 | ### Recommender Systems 101 | 102 | - Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba. [`arxiv`](https://arxiv.org/abs/1803.02349) 103 | - DKN: Deep Knowledge-Aware Network for News Recommendation. [`arxiv`](https://arxiv.org/abs/1801.08284) 104 | - Explainable Recommendation: A Survey and New Perspectives. [`arxiv`](https://arxiv.org/abs/1804.11192) 105 | - FashionNet: Personalized Outfit Recommendation with Deep Neural Network. [`arxiv`](https://arxiv.org/abs/1810.02443) 106 | - Graph Convolutional Neural Networks for Web-Scale Recommender Systems. [`arxiv`](https://arxiv.org/abs/1806.01973) 107 | - Learning Tree-based Deep Model for Recommender Systems. [`arxiv`](https://arxiv.org/abs/1801.02294) 108 | - MARS: Memory Attention-Aware Recommender System. [`arxiv`](https://arxiv.org/abs/1805.07037) 109 | - Offline A/B testing for Recommender Systems. [`arxiv`](https://arxiv.org/abs/1801.07030) 110 | - Ranking Distillation: Learning Compact Ranking Models With High Performance for Recommender System. [`arxiv`](https://arxiv.org/abs/1809.07428) [`code`](https://github.com/graytowne/rank_distill) 111 | - Sequence-Aware Recommender Systems. [`arxiv`](https://arxiv.org/abs/1802.08452) 112 | - Top-K Off-Policy Correction for a REINFORCE Recommender System. [`arxiv`](https://arxiv.org/abs/1812.02353) 113 | 114 | ### Sentiment Analysis 115 | 116 | - Combination of Domain Knowledge and Deep Learning for Sentiment Analysis. [`arxiv`](https://arxiv.org/abs/1806.08760) 117 | - Word2Vec and Doc2Vec in Unsupervised Sentiment Analysis of Clinical Discharge Summaries. [`arxiv`](https://arxiv.org/abs/1805.00352) 118 | 119 | ### Speech Recognition 120 | 121 | - Automatic Detection of Online Jihadist Hate Speech. [`arxiv`](https://arxiv.org/abs/1803.04596) 122 | - Do WaveNets Dream of Acoustic Waves? [`arxiv`](https://arxiv.org/abs/1802.08370) 123 | - End-to-End Speech Recognition From the Raw Waveform. [`arxiv`](https://arxiv.org/abs/1806.07098) 124 | - Large-Scale Visual Speech Recognition. [`arxiv`](https://arxiv.org/abs/1807.05162) 125 | - Speech Emotion Recognition with Data Augmentation and Layer-wise Learning Rate Adjustment. [`arxiv`](https://arxiv.org/abs/1802.05630) 126 | - Stochastic WaveNet: A Generative Latent Variable Model for Sequential Data. [`arxiv`](https://arxiv.org/abs/1806.06116) 127 | - VoxCeleb2: Deep Speaker Recognition. [`arxiv`](https://arxiv.org/abs/1806.05622) 128 | 129 | ### Text Classification 130 | 131 | - Clinical Text Classification with Rule-based Features and Knowledge-guided Convolutional Neural Networks. [`arxiv`](https://arxiv.org/abs/1807.07425) 132 | - Fake News Identification on Twitter with Hybrid CNN and RNN Models. [`arxiv`](https://arxiv.org/abs/1806.11316) 133 | - Fine-tuned Language Models for Text Classification. [`arxiv`](https://arxiv.org/abs/1801.06146) 134 | - Joint Embedding of Words and Labels for Text Classification. [`arxiv`](https://arxiv.org/pdf/1805.04174.pdf) [`code`](https://github.com/guoyinwang/LEAM) 135 | - Online Embedding Compression for Text Classification using Low Rank Matrix Factorization. [`arxiv`](https://arxiv.org/abs/1811.00641) 136 | - SoPa: Bridging CNNs, RNNs, and Weighted Finite-State Machines. [`arxiv`](https://arxiv.org/abs/1805.06061) [`code`](https://github.com/Noahs-ARK/soft_patterns) 137 | -------------------------------------------------------------------------------- /papers/2018/rl.md: -------------------------------------------------------------------------------- 1 | # Deep Reinforcement Learning 2 | 3 | - Accelerated Methods for Deep Reinforcement Learning. [`arxiv`](https://arxiv.org/abs/1803.02811) 4 | - A Deep Reinforcement Learning Chatbot (Short Version). [`arxiv`](https://arxiv.org/abs/1801.06700) 5 | - AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree Search. [`arxiv`](https://arxiv.org/abs/1805.07440) :star: 6 | - A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress. [`arxiv`](https://arxiv.org/abs/1806.06877) 7 | - Composable Deep Reinforcement Learning for Robotic Manipulation. [`arxiv`](https://arxiv.org/abs/1803.06773) 8 | - Cooperative Multi-Agent Reinforcement Learning for Low-Level Wireless Communication. [`arxiv`](https://arxiv.org/abs/1801.04541) 9 | - Deep Q learning for fooling neural networks. [`arxiv`](https://arxiv.org/abs/1811.05521) 10 | - Deep Reinforcement Fuzzing. [`arxiv`](https://arxiv.org/abs/1801.04589) 11 | - Deep Reinforcement Learning of Cell Movement in the Early Stage of C. elegans Embryogenesis. [`arxiv`](https://arxiv.org/abs/1801.04600) 12 | - Deep Reinforcement Learning For Sequence to Sequence Models. [`arxiv`](https://arxiv.org/abs/1805.09461) [`code`](https://github.com/yaserkl/RLSeq2Seq/) 13 | - Deep Reinforcement Learning for Vision-Based Robotic Grasping: A Simulated Comparative Evaluation of Off-Policy Methods. [`arxiv`](https://arxiv.org/abs/1802.10264) 14 | - Deep Reinforcement Learning in Portfolio Management. [`arxiv`](https://arxiv.org/abs/1808.09940) [`code`](https://github.com/qq303067814/Reinforcement-learning-in-portfolio-management-) 15 | - Deep Reinforcement Learning using Capsules in Advanced Game Environments. [`arxiv`](https://arxiv.org/abs/1801.09597) 16 | - Deep Reinforcement Learning with Model Learning and Monte Carlo Tree Search in Minecraft. [`arxiv`](https://arxiv.org/abs/1803.08456) 17 | - Distributed Deep Reinforcement Learning: Learn how to play Atari games in 21 minutes. [`arxiv`](https://arxiv.org/abs/1801.02852) [`code`](https://github.com//anonymous-author1/DDRL) 18 | - Diversity is All You Need: Learning Skills without a Reward Function. [`arxiv`](https://arxiv.org/abs/1802.06070) 19 | - Faster Deep Q-learning using Neural Episodic Control. [`arxiv`](https://arxiv.org/abs/1801.01968) 20 | - Feedback-Based Tree Search for Reinforcement Learning. [`arxiv`](https://arxiv.org/abs/1805.05935) 21 | - Feudal Reinforcement Learning for Dialogue Management in Large Domains. [`arxiv`](https://arxiv.org/abs/1803.03232) 22 | - Forward-Backward Reinforcement Learning. [`arxiv`](https://arxiv.org/abs/1803.10227) 23 | - Graph Convolutional Reinforcement Learning for Multi-Agent Cooperation. [`arxiv`](https://arxiv.org/abs/1810.09202) 24 | - Hierarchical Reinforcement Learning: Approximating Optimal Discounted TSP Using Local Policies. [`arxiv`](https://arxiv.org/abs/1803.04674) 25 | - IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures. [`arxiv`](https://arxiv.org/abs/1802.01561) 26 | - Kickstarting Deep Reinforcement Learning. [`arxiv`](https://arxiv.org/abs/1803.03835) 27 | - Learning a Prior over Intent via Meta-Inverse Reinforcement Learning. [`arxiv`](https://arxiv.org/abs/1805.12573) 28 | - Meta Reinforcement Learning with Distribution of Exploration Parameters Learned by Evolution Strategies. [`arxiv`](https://arxiv.org/abs/1812.11314) 29 | - Meta Reinforcement Learning with Latent Variable Gaussian Processes. [`arxiv`](https://arxiv.org/abs/1803.07551) 30 | - Multi-Agent Reinforcement Learning: A Report on Challenges and Approaches. [`arxiv`](https://arxiv.org/abs/1807.09427) 31 | - Pretraining Deep Actor-Critic Reinforcement Learning Algorithms With Expert Demonstrations. [`arxiv`](https://arxiv.org/abs/1801.10459) 32 | - Psychlab: A Psychology Laboratory for Deep Reinforcement Learning Agents. [`arxiv`](https://arxiv.org/abs/1801.08116) 33 | - Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning. [`arxiv`](https://arxiv.org/abs/1802.06501) 34 | - Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review. [`arxiv`](https://arxiv.org/abs/1805.00909) 35 | - Reinforcement Learning from Imperfect Demonstrations. [`arxiv`](https://arxiv.org/abs/1802.05313) 36 | - Reinforcement Learning to Rank in E-Commerce Search Engine: Formalization, Analysis, and Application. [`arxiv`](https://arxiv.org/abs/1803.00710) 37 | - RUDDER: Return Decomposition for Delayed Rewards. [`arxiv`](https://arxiv.org/abs/1806.07857) [`code`](https://github.com/ml-jku/baselines-rudder) 38 | - Semi-parametric Topological Memory for Navigation. [`arxiv`](https://arxiv.org/abs/1803.00653) [`tensorflow`](https://github.com/nsavinov/SPTM) 39 | - Shared Autonomy via Deep Reinforcement Learning. [`arxiv`](https://arxiv.org/abs/1802.01744) 40 | - Setting up a Reinforcement Learning Task with a Real-World Robot. [`arxiv`](https://arxiv.org/abs/1803.07067) 41 | - Simple random search provides a competitive approach to reinforcement learning. [`arxiv`](https://arxiv.org/abs/1803.07055) [`code`](https://github.com/modestyachts/ARS) 42 | - Unsupervised Meta-Learning for Reinforcement Learning. [`arxiv`](https://arxiv.org/abs/1806.04640) 43 | - Using reinforcement learning to learn how to play text-based games. [`arxiv`](https://arxiv.org/abs/1801.01999) -------------------------------------------------------------------------------- /papers/2019/cv.md: -------------------------------------------------------------------------------- 1 | # Computer Vision 2 | 3 | - DSConv: Efficient Convolution Operator. [`arxiv`](https://arxiv.org/abs/1901.01928) 4 | - EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. [`arxiv`](https://arxiv.org/abs/1905.11946) [`code`](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) 5 | - Exploring Randomly Wired Neural Networks for Image Recognition. [`arxiv`](https://arxiv.org/abs/1904.01569) 6 | - FPGA-based Accelerators of Deep Learning Networks for Learning and Classification: A Review. [`arxiv`](https://arxiv.org/abs/1901.00121) 7 | - Monocular Total Capture: Posing Face, Body, and Hands in the Wild. [`arxiv`](https://arxiv.org/abs/1812.01598) 8 | - Semi-Supervised Learning with Self-Supervised Networks. [`arxiv`](https://arxiv.org/abs/1906.10343) [`code`](https://github.com/vuptran/sesemi) 9 | 10 | ## GAN 11 | 12 | - A Survey on GANs for Anomaly Detection. [`arxiv`](https://arxiv.org/abs/1906.11632) 13 | - Exploring Randomly Wired Neural Networks for Image Recognition. [`arxiv`](https://arxiv.org/abs/1904.01184) 14 | 15 | ## PreProcessing 16 | 17 | - Learning Data Augmentation Strategies for Object Detection. [`arxiv`](https://arxiv.org/abs/1906.11172) 18 | 19 | ## Object Detection 20 | 21 | - Consistent Optimization for Single-Shot Object Detection. [`arxiv`](https://arxiv.org/abs/1901.06563) 22 | - DetNAS: Backbone Search for Object Detection. [`arxiv`](https://arxiv.org/pdf/1903.10979.pdf) 23 | - EfficientDet: Scalable and Efficient Object Detection. [`arxiv`](https://arxiv.org/pdf/1911.09070v1.pdf) [`code`](https://github.com/xuannianz/EfficientDet) 24 | - GAN-Knowledge Distillation for one-stage Object Detection. [`arxiv`](https://arxiv.org/abs/1906.08467) 25 | - NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection. [`arxiv`](https://arxiv.org/pdf/1904.07392.pdf) 26 | 27 | ## Semantic Segmentation 28 | 29 | - CenterMask : Real-Time Anchor-Free Instance Segmentation. [`arxiv`](https://arxiv.org/abs/1911.06667) [`code`](https://github.com/youngwanLEE/centermask2) 30 | - Gated-SCNN: Gated Shape CNNs for Semantic Segmentation. [`arxiv`](https://arxiv.org/abs/1907.05740) [`code`](https://github.com/nv-tlabs/GSCNN) 31 | - Object-Contextual Representations for Semantic Segmentation. [`arxiv`](https://arxiv.org/abs/1909.11065) [`code`](https://github.com/rosinality/ocr-pytorch) 32 | -------------------------------------------------------------------------------- /papers/2019/dl.md: -------------------------------------------------------------------------------- 1 | # Deep Learning 2 | 3 | - A First Look at the Crypto-Mining Malware Ecosystem: A Decade of Unrestricted Wealth. [`arxiv`](https://arxiv.org/abs/1901.00846) 4 | - A Gentle Introduction to Deep Learning for Graphs. [`arxiv`](https://arxiv.org/abs/1912.12693) 5 | - AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty. [`arxiv`](https://arxiv.org/pdf/1912.02781.pdf) [`code`](https://github.com/google-research/augmix) :star: 6 | - diffGrad: An Optimization Method for Convolutional Neural Networks. [`arxiv`](https://arxiv.org/abs/1909.11015) [`code`](https://github.com/lessw2020/Best-Deep-Learning-Optimizers) 7 | - LiSHT: Non-Parametric Linearly Scaled Hyperbolic Tangent Activation Function for Neural Networks. [`arxiv`](https://arxiv.org/abs/1901.05894) 8 | - One-Class Convolutional Neural Network. [`arxiv`](https://arxiv.org/abs/1901.08688) 9 | - On the effect of the activation function on the distribution of hidden nodes in a deep network. [`arxiv`](https://arxiv.org/abs/1901.02104) 10 | - TactileGCN: A Graph Convolutional Network for Predicting Grasp Stability with Tactile Sensors. [`arxiv`](https://arxiv.org/abs/1901.06181) 11 | 12 | ## Attention 13 | 14 | - Attentive Neural Processes. [`arxiv`](https://arxiv.org/abs/1901.05761) :star: 15 | - FAN: Focused Attention Networks. [`arxiv`](https://arxiv.org/abs/1905.11498) 16 | 17 | ## Auto ML 18 | 19 | - A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments. [`arxiv`](https://arxiv.org/abs/1911.00294) 20 | - Combinatorial Bayesian Optimization using the Graph Cartesian Product. [`arxiv`](https://arxiv.org/abs/1902.00448) [`code`](https://github.com/QUVA-Lab/COMBO) 21 | - EAT-NAS: Elastic Architecture Transfer for Accelerating Large-scale Neural Architecture Search. [`arxiv`](https://arxiv.org/abs/1901.05884v1) 22 | 23 | 24 | ## Transfer Learning 25 | 26 | - Time Series Anomaly Detection Using Convolutional Neural Networks and Transfer Learning. [`arxiv`](https://arxiv.org/abs/1905.13628) 27 | - Virtual-to-Real-World Transfer Learning for Robots on Wilderness Trails. [`arxiv`](https://arxiv.org/abs/1901.05599) -------------------------------------------------------------------------------- /papers/2019/nlp.md: -------------------------------------------------------------------------------- 1 | # Nature language process 2 | 3 | - Aspect Specific Opinion Expression Extraction using Attention based LSTM-CRF Network. [`arxiv`](https://arxiv.org/abs/1902.02709) 4 | - BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning. [`arxiv`](https://arxiv.org/abs/1902.02671) 5 | - BioBERT: pre-trained biomedical language representation model for biomedical text mining. [`arxiv`](https://arxiv.org/abs/1901.08746) 6 | - Cross-lingual Language Model Pretraining. [`arxiv`](https://arxiv.org/abs/1901.07291) 7 | - Does BERT Make Any Sense? Interpretable Word Sense Disambiguation with Contextualized Embeddings. [`arxiv`](https://arxiv.org/abs/1909.10430) [`code`](https://github.com/uhh-lt/bert-sense) 8 | - GILT: Generating Images from Long Text. [`arxiv`](https://arxiv.org/abs/1901.02404) 9 | - Open Research Knowledge Graph: Towards Machine Actionability in Scholarly Communication. [`arxiv`](https://arxiv.org/abs/1901.10816) 10 | - SumQE: a BERT-based Summary Quality Estimation Model. [`arxiv`](https://arxiv.org/abs/1909.00578) [`code`](https://github.com/nlpaueb/SumQE) 11 | - Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context. [`arxiv`](https://arxiv.org/abs/1901.02860) [`code`](https://github.com/kimiyoung/transformer-xl) 12 | 13 | ## Embedding 14 | 15 | - A Multi-Resolution Word Embedding for Document Retrieval from Large Unstructured Knowledge Bases. [`arxiv`](https://arxiv.org/abs/1902.00663) 16 | - Word Embeddings for Sentiment Analysis: A Comprehensive Empirical Survey. [`arxiv`](https://arxiv.org/abs/1902.00753) 17 | - Fast Transformer Decoding: One Write-Head is All You Need. [`arxiv`](https://arxiv.org/abs/1911.02150) 18 | 19 | ## NMT 20 | 21 | - Modeling Latent Sentence Structure in Neural Machine Translation. [`arxiv`](https://arxiv.org/abs/1901.06436) 22 | - Training on Synthetic Noise Improves Robustness to Natural Noise in Machine Translation. [`arxiv`](https://arxiv.org/abs/1902.01509) 23 | 24 | ## Reading Comprehension 25 | 26 | - DREAM: A Challenge Dataset and Models for Dialogue-Based Reading Comprehension. [`arxiv`](https://arxiv.org/abs/1902.00164) 27 | - Review Conversational Reading Comprehension. [`arxiv`](https://arxiv.org/abs/1902.00821) 28 | 29 | 30 | ## Recommender Systems 31 | 32 | - Behavior Sequence Transformer for E-commerce Recommendation in Alibaba. [`arxiv`](https://arxiv.org/pdf/1905.06874.pdf) 33 | 34 | ## Text Classification 35 | 36 | - Delta-training: Simple Semi-Supervised Text Classification using Pretrained Word Embeddings. [`arxiv`](https://arxiv.org/abs/1901.07651) 37 | - EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks. [`arxiv`](https://arxiv.org/abs/1901.11196) [`code`](https://github.com/jasonwei20/eda_nlp) 38 | - Hierarchical Multi-label Text Classification: An Attention-based Recurrent Network Approach. [`pdf`](https://dl.acm.org/doi/pdf/10.1145/3357384.3357885?download=true) [`code`](https://github.com/RandolphVI/Hierarchical-Multi-Label-Text-Classification) -------------------------------------------------------------------------------- /papers/2019/rl.md: -------------------------------------------------------------------------------- 1 | # Deep Reinforcement Learning 2 | 3 | - Certified Reinforcement Learning with Logic Guidance. [`arxiv`](https://arxiv.org/abs/1902.00778) 4 | - Go-Explore: a New Approach for Hard-Exploration Problems. [`arxiv`](https://arxiv.org/abs/1901.10995) 5 | - Gym-Ignition: Reproducible Robotic Simulations for Reinforcement Learning. [`arxiv`](https://arxiv.org/abs/1911.01715) [`code`](https://github.com/robotology/gym-ignition) 6 | - Hierarchical Reinforcement Learning for Multi-agent MOBA Game. [`arxiv`](https://arxiv.org/abs/1901.08004) 7 | - Learning Action Representations for Reinforcement Learning. [`arxiv`](https://arxiv.org/abs/1902.00183) 8 | - Modern Deep Reinforcement Learning Algorithms. [`arxiv`](https://arxiv.org/abs/1906.10025) 9 | - Motion Perception in Reinforcement Learning with Dynamic Objects. [`arxiv`](https://arxiv.org/abs/1901.03162) 10 | -------------------------------------------------------------------------------- /papers/2020/cv.md: -------------------------------------------------------------------------------- 1 | # cv 2 | 3 | - Joint Deep Learning of Facial Expression Synthesis and Recognition. [`arxiv`](https://arxiv.org/pdf/2002.02194.pdf) 4 | - U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation. [`arxiv`](https://arxiv.org/abs/1907.10830) [`code`](https://github.com/znxlwm/UGATIT-pytorch) :star: 5 | 6 | ## Contrastive Learning 7 | 8 | - A Simple Framework for Contrastive Learning of Visual Representations. [`arxiv`](https://arxiv.org/pdf/2002.05709.pdf) 9 | 10 | ## Object Detect 11 | 12 | - YOLOv4: Optimal Speed and Accuracy of Object Detection. [`arxiv`](https://arxiv.org/pdf/2004.10934.pdf) [`code`](https://github.com/AlexeyAB/darknet) 13 | 14 | ## Video 15 | 16 | - Adversarial Distortion for Learned Video Compression. [`arxiv`](https://arxiv.org/abs/2004.09508) 17 | - A Review on Deep Learning Techniques for Video Prediction. [`arxiv`](https://arxiv.org/abs/2004.05214) 18 | - FineGym: A Hierarchical Video Dataset for Fine-grained Action Understanding. [`arxiv`](https://arxiv.org/abs/2004.06704) 19 | - Labelling unlabelled videos from scratch with multi-modal self-supervision. [`arxiv`](https://arxiv.org/abs/2006.13662) 20 | - Multi-modal Self-Supervision from Generalized Data Transformations. [`arxiv`](https://arxiv.org/abs/2003.04298) 21 | - Unsupervised Multimodal Video-to-Video Translation via Self-Supervised Learning. [`arxiv`](https://arxiv.org/abs/2004.06502) -------------------------------------------------------------------------------- /papers/2020/dl.md: -------------------------------------------------------------------------------- 1 | # Deep Learning 2 | 3 | - Attentive Group Equivariant Convolutional Networks. [`arxiv`](https://arxiv.org/abs/2002.03830) 4 | - Fast Differentiable Sorting and Ranking. [`arxiv`](https://arxiv.org/abs/2002.08871) 5 | - Gradient Boosting Neural Networks: GrowNet. [`arxiv`](https://arxiv.org/abs/2002.07971) 6 | - Learning with Differentiable Perturbed Optimizers. [`arxiv`](https://arxiv.org/abs/2002.08676) 7 | - The Costs and Benefits of Goal-Directed Attention in Deep Convolutional Neural Networks. [`pdf`](https://arxiv.org/pdf/2002.02342.pdf) 8 | - The Geometry of Sign Gradient Descent. [`arxiv`](https://arxiv.org/abs/2002.08056) 9 | - The large learning rate phase of deep learning: the catapult mechanism. [`arxiv`](https://arxiv.org/abs/2003.02218) 10 | - Towards Understanding Hierarchical Learning: Benefits of Neural Representations. [`arxiv`](https://arxiv.org/abs/2006.13436) 11 | 12 | ## AutoML 13 | 14 | - When NAS Meets Robustness: In Search of Robust Architectures against Adversarial Attacks. [`arxiv`](https://arxiv.org/abs/1911.10695) [`code`](https://github.com/gmh14/RobNets) 15 | - Gryffin: An algorithm for Bayesian optimization for categorical variables informed by physical intuition with applications to chemistry. [`arxiv`](https://arxiv.org/abs/2003.12127) 16 | - Uncertainty Quantification for Bayesian Optimization. [`pdf`](https://arxiv.org/pdf/2002.01569.pdf) 17 | 18 | ## GAN 19 | 20 | - BachGAN: High-Resolution Image Synthesis from Salient Object Layout. [`arxiv`](https://arxiv.org/abs/2003.11690) [`code`](https://github.com/Cold-Winter/BachGAN) 21 | - Unbalanced GANs: Pre-training the Generator of Generative Adversarial Network using Variational Autoencoder. [`pdf`](https://arxiv.org/pdf/2002.02112.pdf) 22 | 23 | ## GNN 24 | 25 | - Benchmark and Best Practices for Biomedical Knowledge Graph Embeddings. [`arxiv`](https://arxiv.org/abs/2006.13774) [`code`](https://github.com/dchang56/snomed_kge) 26 | - Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks. [`arxiv`](https://arxiv.org/abs/2003.11702) [`code`](https://github.com/balcilar/Spectral-Designed-Graph-Convolutions) 27 | - Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework. [`arxiv`](https://arxiv.org/abs/2006.13365) 28 | - Generalization and Representational Limits of Graph Neural Networks. [`arxiv`](https://arxiv.org/abs/2002.06157) 29 | - SIGN: Scalable Inception Graph Neural Networks. [`arxiv`](https://arxiv.org/abs/2004.11198) 30 | - StickyPillars: Robust feature matching on point clouds using Graph Neural Networks. [`arxiv`](https://arxiv.org/abs/2002.03983) 31 | - Supervised Learning on Relational Databases with Graph Neural Networks. [`arxiv`](https://arxiv.org/abs/2002.02046) [`code`](https://github.com/mwcvitkovic/Supervised-Learning-on-Relational-Databases-with-GNNs) 32 | 33 | ## Meta Learning 34 | 35 | - A Comprehensive Overview and Survey of Recent Advances in Meta-Learning. [`arxiv`](https://arxiv.org/abs/2004.11149) 36 | - Meta-Learning in Neural Networks: A Survey. [`arxiv`](https://arxiv.org/pdf/2004.05439.pdf) 37 | - Regularizing Meta-Learning via Gradient Dropout. [`arxiv`](https://arxiv.org/abs/2004.05859) -------------------------------------------------------------------------------- /papers/2020/nlp.md: -------------------------------------------------------------------------------- 1 | # Natural Language Model 2 | 3 | - Energy-Based Models for Text. [`arxiv`](https://arxiv.org/abs/2004.10188) 4 | 5 | ## Dialog 6 | 7 | - Recipes for building an open-domain chatbot. [`arxiv`](https://arxiv.org/abs/2004.13637) 8 | 9 | ## Generate Adversarial Model 10 | 11 | - FastWordBug: A Fast Method To Generate Adversarial Text Against NLP Applications. [`arxiv`](https://arxiv.org/pdf/2002.00760.pdf) 12 | 13 | ## Information Extraction 14 | 15 | - Rapid Adaptation of BERT for Information Extraction on Domain-Specific Business Documents. [`arxiv`](https://arxiv.org/pdf/2002.01861.pdf) 16 | 17 | 18 | ## Language Model 19 | 20 | - BERT-of-Theseus: Compressing BERT by Progressive Module Replacing. [`arxiv`](https://arxiv.org/abs/2002.02925) [`code`](https://github.com/JetRunner/BERT-of-Theseus) 21 | - BERTweet: A pre-trained language model for English Tweets. [`arxiv`](https://arxiv.org/abs/2005.10200) [`code`](https://github.com/VinAIResearch/BERTweet) 22 | - Blank Language Models. [`arxiv`](https://arxiv.org/abs/2002.03079) 23 | - Controlling Computation versus Quality for Neural Sequence Models. [`arxiv`](https://arxiv.org/abs/2002.07106) 24 | - ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators. [`arxiv`](https://openreview.net/pdf?id=r1xMH1BtvB) [`code`](https://github.com/google-research/electra) :star: 25 | - Extending Multilingual BERT to Low-Resource Languages. [`arxiv`](https://arxiv.org/abs/2004.13640) 26 | - Limits of Detecting Text Generated by Large-Scale Language Models. [`arxiv`](https://arxiv.org/abs/2002.03438) 27 | - PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned Generation. [`arxiv`](https://arxiv.org/abs/2004.07159) 28 | - Pretrained Transformers Improve Out-of-Distribution Robustness. [`arxiv`](https://arxiv.org/abs/2004.06100) 29 | - Semantics-aware BERT for Language Understanding. [`arxiv`](https://arxiv.org/pdf/1909.02209.pdf) [`code`](https://github.com/cooelf/SemBERT) 30 | 31 | ## Pos-tagging 32 | 33 | - Joint Embedding in Named Entity Linking on Sentence Level. [`arxiv`](https://arxiv.org/abs/2002.04936) 34 | 35 | ## QA 36 | 37 | - AmbigQA: Answering Ambiguous Open-domain Questions. [`arxiv`](https://arxiv.org/abs/2004.10645) 38 | - Asking and Answering Questions to Evaluate the Factual Consistency of Summaries. [`arxiv`](https://arxiv.org/abs/2004.04228) 39 | - Conversational Question Answering over Passages by Leveraging Word Proximity Networks. [`arxiv`](https://arxiv.org/abs/2004.13117) [`code`](https://github.com/magkai/CROWN) 40 | - Probing Emergent Semantics in Predictive Agents via Question Answering. [`arxiv`](https://arxiv.org/abs/2006.01016) 41 | - Unsupervised Commonsense Question Answering with Self-Talk. [`arxiv`](https://arxiv.org/abs/2004.05483) [`code`](https://github.com/vered1986/self_talk) 42 | 43 | ## Text Classification 44 | 45 | - Light-Weighted CNN for Text Classification. [`arxiv`](https://arxiv.org/pdf/2004.07922.pdf) 46 | - Multi-Label Text Classification using Attention-based Graph Neural Network. [`arxiv`](https://arxiv.org/abs/2003.11644) 47 | 48 | ## Text Generation 49 | 50 | - NUBIA: NeUral Based Interchangeability Assessor for Text Generation. [`arxiv`](https://arxiv.org/abs/2004.14667) 51 | - Polarized-VAE: Proximity Based Disentangled Representation Learning for Text Generation. [`arxiv`](https://arxiv.org/abs/2004.10809) 52 | - Reverse Engineering Configurations of Neural Text Generation Models. [`arxiv`](https://arxiv.org/abs/2004.06201) 53 | 54 | -------------------------------------------------------------------------------- /papers/2020/rl.md: -------------------------------------------------------------------------------- 1 | # reinforcement learning 2 | 3 | - ActionSpotter: Deep Reinforcement Learning Framework for Temporal Action Spotting in Videos. [`arxiv`](https://arxiv.org/abs/2004.06971) 4 | - Harnessing Structures for Value-Based Planning and Reinforcement Learning. [`pdf`](https://openreview.net/forum?id=rklHqRVKvH) [`code`](https://github.com/YyzHarry/SV-RL) 5 | - Causally Correct Partial Models for Reinforcement Learning. [`arxiv`](https://arxiv.org/abs/2002.02836) 6 | - Generalized Hidden Parameter MDPs Transferable Model-based RL in a Handful of Trials. [`arxiv`](https://arxiv.org/abs/2002.03072) 7 | - Learning to Fly via Deep Model-Based Reinforcement Learning. [`arxiv`](https://arxiv.org/abs/2003.08876) 8 | - Multi-agent Reinforcement Learning for Networked System Control. [`pdf`](https://openreview.net/forum?id=Syx7A3NFvH) [`code`](https://github.com/cts198859/deeprl_network) 9 | - Off-Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers. [`arxiv`](https://arxiv.org/abs/2006.13916) 10 | - Quantifying Differences in Reward Functions. [`arxiv`](https://arxiv.org/abs/2006.13900) 11 | - RL Unplugged: Benchmarks for Offline Reinforcement Learning. [`arxiv`](https://arxiv.org/abs/2006.13888) 12 | -------------------------------------------------------------------------------- /papers/2021/cv.md: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/endymecy/awesome-deeplearning-resources/0a50c347b11f338bcb727aa8d182e263f6663b3b/papers/2021/cv.md -------------------------------------------------------------------------------- /papers/2021/mm.md: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/endymecy/awesome-deeplearning-resources/0a50c347b11f338bcb727aa8d182e263f6663b3b/papers/2021/mm.md -------------------------------------------------------------------------------- /papers/2021/nlp.md: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/endymecy/awesome-deeplearning-resources/0a50c347b11f338bcb727aa8d182e263f6663b3b/papers/2021/nlp.md -------------------------------------------------------------------------------- /papers/before-2010.md: -------------------------------------------------------------------------------- 1 | ## before 2010 2 | 3 | - A fast learning algorithm for deep belief nets. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjmxcXKjtDQAhVEjpQKHU5uAVkQFgggMAA&url=https%3A%2F%2Fwww.cs.toronto.edu%2F~hinton%2Fabsps%2Ffastnc.pdf&usg=AFQjCNELT7wVLLgpvARk6bCARpfzwWUOLg)] :star: 4 | - A Tutorial on Energy-Based Learning. [[url]](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwiBk8i0jtDQAhVFJJQKHffxCp0QFggdMAA&url=http%3A%2F%2Fyann.lecun.com%2Fexdb%2Fpublis%2Fpdf%2Flecun-06.pdf&usg=AFQjCNHSCWGMFSXY4CSmXrAC4UpJD6izOw) 5 | - [LeNet] Gradient-based learning applied to document recognition. [[pdf](http://yann.lecun.com/exdb/lenet/)] :star: 6 | - Constructing Informative Priors using Transfer Learning. [[url](http://ai.stanford.edu/~ang/papers/icml06-transferinformativepriors.pdf)] 7 | - Connectionist Temporal Classification: Labelling unsegmented Sequence Data with Recurrent Neural Networks. [[url](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjf9J2yoo7RAhUoxFQKHZFPD_wQFggfMAA&url=http%3A%2F%2Fmachinelearning.wustl.edu%2Fmlpapers%2Fpaper_files%2Ficml2006_GravesFGS06.pdf&usg=AFQjCNFrqG2eQSvESxvp7EhHYfe9y-gH_Q)] 8 | - Deep Boltzmann Machines. [[url](http://www.utstat.toronto.edu/~rsalakhu/papers/dbm.pdf)] :star: 9 | - Exploring Strategies for Training Deep Neural Networks. [[url]](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwiawMrP7NDQAhXJTLwKHeZzBxgQFgggMAA&url=http%3A%2F%2Fdeeplearning.cs.cmu.edu%2Fpdfs%2F1111%2Fjmlr10_larochelle.pdf&usg=AFQjCNE9A4CWIZpcCM4FFVcB5lWL-49mlw) 10 | - Efficient Learning of Sparse Representations with an Energy-Based Model. [[url]](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjSloOhjtDQAhVBRJQKHaWRAicQFgghMAA&url=https%3A%2F%2Fpapers.nips.cc%2Fpaper%2F3112-efficient-learning-of-sparse-representations-with-an-energy-based-model.pdf&usg=AFQjCNFZs1ap9T-WHpdAUtFgX2aFs-38sg) :star: 11 | - Efficient sparse coding algorithms. [[url]](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwipv5r1kNDQAhVDGZQKHXXjC-cQFggiMAA&url=http%3A%2F%2Fpapers.nips.cc%2Fpaper%2F2979-efficient-sparse-coding-algorithms.pdf&usg=AFQjCNEZEP5SxMogeVfZA0mmECXQzQXfqQ) :star: 12 | - Energy-Based Models in Document Recognition and Computer Vision. [[url]](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwipucXYkNDQAhUDj5QKHcTrCKMQFggjMAA&url=http%3A%2F%2Fyann.lecun.com%2Fexdb%2Fpublis%2Fpdf%2Flecun-icdar-keynote-07.pdf&usg=AFQjCNFXXuq-tKKteAowMiWkRLhhBl89nA) 13 | - Extracting and Composing Robust Features with Denoising Autoencoders. [[url]](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwiLicmC7NDQAhXFXrwKHY0ADcUQFgggMAA&url=http%3A%2F%2Fmachinelearning.org%2Farchive%2Ficml2008%2Fpapers%2F592.pdf&usg=AFQjCNHhfwA6PKI3gKjnBc36z7Jqs7d0mw) :star: 14 | - Fast Inference in Sparse Coding Algorithms with Applications to Object Recognition. [[url]](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&uact=8&ved=0ahUKEwjByO3069DQAhVET7wKHcwSDFQQFggvMAE&url=https%3A%2F%2Farxiv.org%2Fpdf%2F1010.3467&usg=AFQjCNHcNp5zQf6YypllW96kWFpXXMCB7g) 15 | - Gaussian Process Models for Link Analysis and Transfer Learning. [[url](http://papers.nips.cc/paper/3284-gaussian-process-models-for-link-analysis-and-transfer-learning.pdf)] 16 | - Greedy Layer-Wise Training of Deep Networks. [[url]](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwic_va3kNDQAhWDF5QKHdw7A-YQFgggMAA&url=https%3A%2F%2Fpapers.nips.cc%2Fpaper%2F3048-greedy-layer-wise-training-of-deep-networks.pdf&usg=AFQjCNEKqhptR9m0CF7Ygu6UhJD3teRXnQ) :star: 17 | - Learning Invariant Features through Topographic Filter Maps. [[url]](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjPn5m07NDQAhUIvrwKHY0WCKoQFgggMAA&url=http%3A%2F%2Fwww.cs.toronto.edu%2F~ranzato%2Fpublications%2Fkavukcuoglu-cvpr09.pdf&usg=AFQjCNFsBjrEyT8Ct8hlBz2h82ngVcp_wA) 18 | - Linear Spatial Pyramid Matching Using Sparse Coding for Image Classification. [[url]](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwiGkqSn7NDQAhWCa7wKHWtwBSUQFggjMAA&url=http%3A%2F%2Fwww.ifp.illinois.edu%2F~jyang29%2Fpapers%2FCVPR09-ScSPM.pdf&usg=AFQjCNFJtIxHQ6evWkvnfax-9HVg4G8SdQ) :star: 19 | - Mapping and Revising Markov Logic Networks for Transfer Learning. [[url](http://www.aaai.org/Papers/AAAI/2007/AAAI07-096.pdf)] 20 | - Nonlinear Learning using Local Coordinate Coding. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwiyobCZ7NDQAhVFgrwKHQ_EC1kQFgggMAA&url=http%3A%2F%2Fece.duke.edu%2F~lcarin%2Fnips09_lcc.pdf&usg=AFQjCNFWboAUuKb5D50fnZFVsx4TWuCwTw)] :star: 21 | - Notes on Convolutional Neural Networks. [[url]](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwiuh-SFjtDQAhUMjZQKHQ8xAxAQFggiMAA&url=http%3A%2F%2Fcogprints.org%2F5869%2F1%2Fcnn_tutorial.pdf&usg=AFQjCNGqmw7vLOJXSwyHyS6SPTDD5VOiGg) 22 | - Reducing the Dimensionality of Data with Neural Networks. [[science](http://science.sciencemag.org/content/313/5786/504)] :star: 23 | - To Recognize Shapes, First Learn to Generate Images. [[url]](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjNk4jKjNDQAhUMppQKHROED1cQFgggMAA&url=http%3A%2F%2Fwww.cs.toronto.edu%2F~fritz%2Fabsps%2FmontrealTR.pdf&usg=AFQjCNGmWlKfMB2j-3PWensTW6Q6k9A1uA) 24 | - Scaling Learning Algorithms towards AI. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjmk-SjkNDQAhVFipQKHapJAKQQFggiMAA&url=http%3A%2F%2Fyann.lecun.com%2Fexdb%2Fpublis%2Fpdf%2Fbengio-lecun-07.pdf&usg=AFQjCNGsg3RffgzLebvpoqnCMK7BFEA-3A)] :star: 25 | - Sparse deep belief net model for visual area V2. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjTr5b4j9DQAhUTv5QKHROTAHMQFgggMAA&url=http%3A%2F%2Fai.stanford.edu%2F~ang%2Fpapers%2Fnips07-sparsedeepbeliefnetworkv2.pdf&usg=AFQjCNHRZL9gavkOrCmx0OdMzD9blaUC8Q)] :star: 26 | - Sparse Feature Learning for Deep Belief Networks. [[url]](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwiNiK3Wj9DQAhUBt5QKHVOsDKIQFgggMAA&url=https%3A%2F%2Fpapers.nips.cc%2Fpaper%2F3363-sparse-feature-learning-for-deep-belief-networks.pdf&usg=AFQjCNFNpWYDTG49fBdogmG-7L4tDgM7kQ) 27 | - Training restricted Boltzmann machines using approximations to the likelihood gradient. [[url]](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjH1Kvp69DQAhVEVbwKHVo8A70QFgggMAA&url=http%3A%2F%2Fwww.machinelearning.org%2Farchive%2Ficml2008%2Fpapers%2F638.pdf&usg=AFQjCNF2KMp3ZrdqkeUwe0v_jYoEGmuPDg) 28 | - Training Products of experts by minimizing contrastive divergence. [[url]](Training Products of Experts by Minimizing Contrastive Divergence)] :star: 29 | - Using Fast Weights to Improve Persistent Contrastive Divergence. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwiNt4WO7NDQAhVGybwKHR5yC6sQFgggMAA&url=http%3A%2F%2Fwww.cs.toronto.edu%2F~tijmen%2Ffpcd%2Ffpcd.ps.gz&usg=AFQjCNEfcQVHseNmdUyK1q6nVcyYM-9-dQ)] :star: 30 | - Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjjrqmLj9DQAhUEI5QKHWe2CCcQFgggMAA&url=http%3A%2F%2Fyann.lecun.com%2Fexdb%2Fpublis%2Fpdf%2Franzato-cvpr-07.pdf&usg=AFQjCNFwvKjLRcBMth7fYufJcCZzmirlOw)] 31 | - What is the Best Multi-Stage Architecture for Object Recognition?. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwi5pMD46tDQAhUBxrwKHcVNBSUQFggdMAA&url=http%3A%2F%2Fyann.lecun.com%2Fexdb%2Fpublis%2Fpdf%2Fjarrett-iccv-09.pdf&usg=AFQjCNFNWVpfiBL6O_xYc_FzAf2RF2VyTw)] :star: 32 | 33 | ### Transfer learning 34 | 35 | - A Survey on Transfer Learning. [[url]](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=3&cad=rja&uact=8&ved=0ahUKEwj2zKuZr47RAhVqqVQKHWk0BtYQFgg5MAI&url=%68%74%74%70%3a%2f%2f%63%73%2e%67%6d%75%2e%65%64%75%2f%7e%63%61%72%6c%6f%74%74%61%2f%74%65%61%63%68%69%6e%67%2f%43%53%37%37%35%2d%73%31%30%2f%72%65%61%64%69%6e%67%73%2f%74%72%61%6e%73%66%65%72%6c%65%61%72%6e%69%6e%67%2e%70%64%66&usg=AFQjCNFwymAVN8XeRhulhDnWBSc_ErtzYA)] :star: 36 | - Modeling Transfer Relationships Between Learning Tasks for Improved Inductive Transfer. [[pdf](http://maple.cs.umbc.edu/papers/ModelingTransferRelationships.pdf)] 37 | - To Transfer or Not To Transfer.[[url](http://web.engr.oregonstate.edu/~tgd/publications/rosenstein-marx-kaelbling-dietterich-hnb-nips2005-transfer-workshop.pdf)] 38 | - Transfer learning for text classification. [[url](http://robotics.stanford.edu/~ang/papers/nips05-transfer.pdf)] 39 | - Transfer learning for collaborative filtering via a rating-matrix generative model.[[url](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=4&cad=rja&uact=8&ved=0ahUKEwjNs6n5so7RAhVJrVQKHbKsDFEQFghEMAM&url=http%3A%2F%2Fvideolectures.net%2Fsite%2Fnormal_dl%2Ftag%3D47942%2Ficml09_li_tlcfvrmgm_01.pdf&usg=AFQjCNGUCLWwmiR1zhCb6L_BzmMHyB6z5w)] 40 | - Transfer learning from multiple source domains via consensus regularization. [[url](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=3&cad=rja&uact=8&ved=0ahUKEwiloLWpuo7RAhWqrFQKHbyMD-kQFggwMAI&url=http%3A%2F%2Fwww3.ntu.edu.sg%2Fhome%2Fsinnopan%2Fpublications%2F%5BEMCL14%5DTransfer%2520Learning%2520with%2520Multiple%2520Sources%2520via%2520Consensus%2520Regularized%2520Autoencoders.pdf&usg=AFQjCNFi6ftA2gms7vi71-ktkt_WTum46Q)] 41 | - Transfer Learning for Reinforcement Learning Domains: A Survey. [[url](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwiu9KmlsY7RAhXJiVQKHTs_B34QFggfMAA&url=http%3A%2F%2Fwww.cs.utexas.edu%2F~ai-lab%2Fpubs%2FJMLR09-taylor.pdf&usg=AFQjCNH-Irnjnro_TgQx2T9D8mAz4KwHhw)] :star: 42 | - [Zero-Shot] Zero-Shot Learning with Semantic Output Codes. [`pdf`](http://www.cs.cmu.edu/afs/cs/project/theo-73/www/papers/zero-shot-learning.pdf) :star: 43 | 44 | #### Instance transfer 45 | 46 | - An improved categorization of classifier’s sensitivity on sample selection bias. [[pdf](https://pdfs.semanticscholar.org/c43c/f8d7fbb41bb9c415f83574ea251b0273acbe.pdf)] 47 | - Boosting for transfer learning. [[url](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjb4qT6r47RAhVDl1QKHfYYAfIQFgghMAA&url=http%3A%2F%2Fftp.cse.ust.hk%2F~qyang%2FDocs%2F2007%2Ftradaboost.pdf&usg=AFQjCNHrEGRLIvusHTEIwYmIGRUsUqeuPw)] :star: 48 | - A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. [[pdf](http://www.face-rec.org/algorithms/Boosting-Ensemble/decision-theoretic_generalization.pdf)] :star: 49 | - Correcting sample selection bias by unlabeled data. [[pdf](https://papers.nips.cc/paper/3075-correcting-sample-selection-bias-by-unlabeled-data.pdf)] 50 | - Cross domain distribution adaptation via kernel mapping. [[pdf](http://xueshu.baidu.com/s?wd=paperuri%3A%28561d2ced4ef800e5c475d510a9b9cb2b%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Bjsessionid%3DD2DFDC65A233E29C69866A058656020A%3Fdoi%3D10.1.1.157.9773%26rep%3Drep1%26type%3Dpdf&ie=utf-8&sc_us=6113889194888417404)] 51 | - Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation.[[pdf](https://papers.nips.cc/paper/3248-direct-importance-estimation-with-model-selection-and-its-application-to-covariate-shift-adaptation.pdf)] 52 | - Discriminative learning for differing training and test distributions. [[pdf](http://machinelearning.org/proceedings/icml2007/papers/303.pdf)] 53 | - Domain Adaptation via Transfer Component Analysis. [[pdf](http://home.cse.ust.hk/~qyang/Docs/2009/TCA.pdf)] :star: 54 | - Instance Weighting for Domain Adaptation in NLP. [[pdf](http://sifaka.cs.uiuc.edu/czhai/pub/acl07.pdf)] 55 | - Logistic regression with an auxiliary data source. [[pdf](http://people.ee.duke.edu/~xjliao/paper/ICML05_MigLogit.pdf)] 56 | - Transferring Naive Bayes Classifiers for Text Classification. [[pdf](https://www.cse.ust.hk/~qyang/Docs/2007/daiaaai07.pdf)] 57 | 58 | #### Feature representation transfer 59 | 60 | - A Spectral Regularization Framework for Multi-Task Structure Learning. [[pdf](https://papers.nips.cc/paper/3187-a-spectral-regularization-framework-for-multi-task-structure-learning.pdf)] 61 | - Biographies, bollywood, boom- boxes and blenders: Domain adaptation for sentiment classification. [[pdf](https://www.cs.jhu.edu/~mdredze/publications/sentiment_acl07.pdf)] 62 | - Co-clustering based Classification for Out-of-domain Documents. [[pdf](https://er2004.cse.ust.hk/~qyang/Docs/2007/daikdd.pdf)] :star: 63 | - Domain adaptation with structural correspondence learning. [[pdf](http://john.blitzer.com/papers/emnlp06.pdf)] 64 | - Frustratingly easy domain adaptation. [[pdf](http://www.umiacs.umd.edu/~hal/docs/daume07easyadapt.pdf)] :star: 65 | - Kernel-based inductive transfer. [[pdf](https://pdfs.semanticscholar.org/45b1/32687d62da38ca2ce0a05e4b52bcf51f1f6f.pdf)] 66 | - Learning a meta-level prior for feature relevance from multiple related tasks. [[pdf](http://ai.stanford.edu/~koller/Papers/Lee+al:ICML07.pdf)] 67 | - Multi-task feature and kernel selection for svms. [[pdf](http://www.cs.columbia.edu/~jebara/papers/metalearn.pdf)] 68 | - Multi-task feature learning. [[pdf](https://papers.nips.cc/paper/3143-multi-task-feature-learning.pdf)] :star: 69 | - Self-taught Clustering. [[pdf](http://www.machinelearning.org/archive/icml2008/papers/432.pdf)] 70 | - Self-taught Learning-Transfer Learning from Unlabeled Data. [[url](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjfz7qOkNDQAhWJoZQKHSVzDOgQFggdMAA&url=http%3A%2F%2Fai.stanford.edu%2F~hllee%2Ficml07-selftaughtlearning.pdf&usg=AFQjCNG71_SrmGXuzHiE5Qo2ugmF96NKgw)] :star: 71 | - Spectral domain-transfer learning. [[url](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&uact=8&ved=0ahUKEwjg5ofSs47RAhWGjVQKHbtwB5gQFggsMAE&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fsummary%3Fdoi%3D10.1.1.141.1551&usg=AFQjCNEnGKdMTf64btUV6QsVSn9RgphWLA)] :star: 72 | - Transfer learning via dimensionality reduction. [[pdf](http://home.cse.ust.hk/~qyang/Docs/2008/AAAIsinnoA.pdf)] 73 | 74 | #### Parameter transfer 75 | 76 | - Knowledge transfer via multiple model local structure mapping. [[pdf](http://aisl.umbc.edu/resources/1145.pdf)] 77 | - Learning Gaussian Process Kernels via Hierarchical Bayes. [[pdf](https://papers.nips.cc/paper/2595-learning-gaussian-process-kernels-via-hierarchical-bayes.pdf)] 78 | - Learning to learn with the informative vector machine. [[pdf](ftp://ftp.dcs.shef.ac.uk/home/neil/mtivm.pdf)] 79 | - Multi-task Gaussian Process Prediction. [[pdf](https://papers.nips.cc/paper/3189-multi-task-gaussian-process-prediction.pdf)] 80 | - Regularized multi-task learning. [[pdf](http://www0.cs.ucl.ac.uk/staff/M.Pontil/reading/mt-kdd.pdf)] 81 | - The more you know, the less you learn: from knowledge transfer to one-shot learning of object categories.[[pdf](http://ftp.idiap.ch/pub/courses/EE-700/material/28-11-2012/Tommasi_BMVC_2009.pdf)] 82 | 83 | #### Relational knowledge transfer 84 | 85 | - Deep transfer via second-order markov logic. [[pdf](http://homes.cs.washington.edu/~pedrod/papers/mlc09a.pdf)] 86 | - Mapping and revising markov logic networks for transfer learning. [[pdf](http://dl.acm.org/citation.cfm?id=1619743)] 87 | - Transfer learning by mapping with minimal target data. [[pdf](http://www.cs.utexas.edu/~ml/papers/lily-ws-aaai-08.pdf)] 88 | - Translated learning: Transfer learning across different feature spaces.[[url](http://machinelearning.wustl.edu/mlpapers/paper_files/NIPS2008_0098.pdf)] :star: 89 | 90 | -------------------------------------------------------------------------------- /pre_trained.md: -------------------------------------------------------------------------------- 1 | # Pretrained Model 2 | 3 | * [Aligning the fastText vectors of 78 languages](https://github.com/Babylonpartners/fastText_multilingual) 4 | * [Available pretrained word embeddings](https://github.com/vzhong/embeddings) 5 | * [Inception-v3 of imagenet](http://download.tensorflow.org/models/image/imagenet/inception-v3-2016-03-01.tar.gz) 6 | * [Caffe2 Model Repository](https://github.com/caffe2/models) 7 | * [Chinese Embedding](https://github.com/liuhuanyong/ChineseEmbedding) 8 | * [Chinese word vectors](https://github.com/candlewill/Chinsese_word_vectors) 9 | * [Chinese Word Vectors 中文词向量](https://github.com/Embedding/Chinese-Word-Vectors) 10 | * [Dependency-Based Word Embeddings.](https://levyomer.wordpress.com/2014/04/25/dependency-based-word-embeddings/) 11 | * [Development kit for MIT Scene Parsing Benchmark](https://github.com/CSAILVision/sceneparsing) 12 | * [English word vectors](https://fasttext.cc/docs/en/english-vectors.html) 13 | * [Easy Machine Learning](https://modeldepot.io/) :star: 14 | * [fastText English Word Vectors](https://www.kaggle.com/facebook/fasttext-wikinews) 15 | * [GloVe: Global Vectors for Word Representation](https://github.com/stanfordnlp/GloVe#download-pre-trained-word-vectors) 16 | * [High level network definitions with pre-trained weights in TensorFlow](https://github.com/taehoonlee/tensornets) 17 | * [Model of the deep residual network used for cifar10](https://github.com/apark263/cfmz) 18 | * [Overview of OpenVINO™ Toolkit Pre-Trained Models](https://github.com/opencv/open_model_zoo/blob/2018/intel_models/index.md) 19 | * [Pretrained ConvNets for pytorch: NASNet, ResNeXt, ResNet, InceptionV4, InceptionResnetV2, Xception, DPN, etc.](https://github.com/Cadene/pretrained-models.pytorch) 20 | * [Pre-Trained Doc2Vec Models](https://github.com/jhlau/doc2vec) 21 | * [Pre-trained Show and Tell: A Neural Image Caption Generator](https://github.com/KranthiGV/Pretrained-Show-and-Tell-model) 22 | * [Pre-trained word vectors](https://github.com/facebookresearch/fastText/blob/master/pretrained-vectors.md) :star: 23 | * [Pre-trained word vectors of 30+ languages](https://github.com/Kyubyong/wordvectors) :star: 24 | * [ResNet in TensorFlow Pretrain Model](https://github.com/ry/tensorflow-resnet) 25 | * [Single Image 3D Interpreter Network](https://github.com/jiajunwu/3dinn) 26 | * [Segmentation models Zoo](https://github.com/qubvel/segmentation_models) 27 | * [Source code and pretrained model for webcam pix2pix](https://github.com/memo/webcam-pix2pix-tensorflow) 28 | * [TensorFlow VGG-16 pre-trained model](https://github.com/ry/tensorflow-vgg16) 29 | * [The pretrained models trained on Moments in Time Dataset](https://github.com/metalbubble/moments_models) 30 | * [Trained image classification models for Keras](https://github.com/fchollet/deep-learning-models) 31 | * [VGGNets for Scene Recognition](https://github.com/wanglimin/Places205-VGGNet) 32 | * [Wolfram Neural Net Repository of Neural Network Models](http://resources.wolframcloud.com/NeuralNetRepository) 33 | * [ZF_UNET_224 Pretrained Model](https://github.com/ZFTurbo/ZF_UNET_224_Pretrained_Model) 34 | -------------------------------------------------------------------------------- /software.md: -------------------------------------------------------------------------------- 1 | # Software 2 | 3 | - `Python` 4 | - `Aorun` [Deep Learning over PyTorch](https://github.com/ramon-oliveira/aorun) 5 | - `Chainer` [Chainer bridge the gap between algorithms and implementations of deep learning.](http://chainer.org/) 6 | - `ChainerMN` [ChainerMN: Scalable distributed deep learning with Chainer](https://github.com/chainer/chainermn) 7 | - `ChainerRL` [A deep reinforcement learning library built on top of Chainer.](https://github.com/pfnet/chainerrl) 8 | - `Coach` [Reinforcement Learning Coach by Intel](https://github.com/NervanaSystems/coach) 9 | - `Colossal-AI` [A Unified Deep Learning System for Big Model Era](https://github.com/hpcaitech/ColossalAI) 10 | - `DeepPy` [A Pythonic deep learning framework built on top of NumPy.](https://github.com/andersbll/deeppy) 11 | - `Deepnet` [A GPU-based python implementation of deep learning algorithms.](https://github.com/nitishsrivastava/deepnet) 12 | - `Deepgaze` [A computer vision library for human-computer interaction based on CNNs](https://github.com/mpatacchiola/deepgaze) 13 | - `Determined` [Deep learning training platform with integrated support for distributed training, hyperparameter tuning, smart GPU scheduling, experiment tracking, and a model registry.](https://github.com/determined-ai/determined) 14 | - `Edward` [A library for probabilistic modeling, inference, and criticism.](http://edwardlib.org/) 15 | - `Elephas` [Distributed Deep learning with Keras & Spark.](https://github.com/maxpumperla/elephas) 16 | - `fastai`[The fast.ai deep learning library, lessons, and tutorials](https://github.com/fastai/fastai) 17 | - `Gensim` [Deep learning toolkit implemented in python programming language intended for handling large text collections, using efficient algorithms.](http://radimrehurek.com/gensim/) 18 | - `Hebel` [A library for deep learning with neural networks in Python using GPU acceleration with CUDA through PyCUDA.](https://github.com/hannes-brt/hebel) 19 | - `Keras` [Deep Learning library for Theano and TensorFlow.](https://keras.io/) :star: 20 | - `Kur` [Descriptive Deep Learning.](https://github.com/deepgram/kur) :star: 21 | - `Mujoco-py` [MuJoCo is a physics engine for detailed, efficient rigid body simulations with contacts. mujoco-py allows using MuJoCo from Python 3.](https://github.com/openai/mujoco-py) 22 | - `Neon` [Neon is Nervana's Python based Deep Learning framework](https://github.com/NervanaSystems/neon). 23 | - `Pyclustering` [A Python, C++ data mining clustering, graph coloring algorithms, oscillatory networks, neural networks library.](https://github.com/annoviko/pyclustering) 24 | - `PyTorch` [Tensors and Dynamic neural networks in Python with strong GPU acceleration.](http://pytorch.org/) :star: 25 | - `Scikit-Learn` [Machine learning in Python.](http://scikit-learn.org) :star: 26 | - `Semisup-Learn` [Semi-supervised learning frameworks for Python](https://github.com/tmadl/semisup-learn) 27 | - SerpentAI [Game Agent Framework. Helping you create AIs / Bots to play any game you own](https://github.com/SerpentAI/SerpentAI) 28 | - `Skll` [SciKit-Learn Laboratory (SKLL) makes it easy to run machine learning experiments.](https://github.com/EducationalTestingService/skll) 29 | - `Sonnet` [TensorFlow-based neural network library](https://github.com/deepmind/sonnet) 30 | - `Tensorflow` [An open source software library for numerical computation using data flow graph by Google](https://www.tensorflow.org/) :star: 31 | - `Polyaxon` [Deep Learning library for TensorFlow for building end to end models and experiments.](https://github.com/polyaxon/polyaxon) 32 | - `TensorFX` [An end to end application framework to simplifies machine learning with TensorFlow](https://github.com/TensorLab/tensorfx) 33 | - `Theano` [Mathematical library in Python, maintained by LISA lab](http://deeplearning.net/software/theano/) 34 | - `Pylearn2` [Theano-based deep learning libraries](http://deeplearning.net/software/pylearn2/) 35 | - `Blocks` [A framework that helps you build neural network models on top of Theano](https://github.com/mila-udem/blocks) :star: 36 | - `Lasagne` [Lightweight library to build and train neural networks in Theano.](https://github.com/Lasagne/Lasagne) 37 | - `WebDNN` [Fastest DNN Execution Framework on Web Browser](https://mil-tokyo.github.io/webdnn/) 38 | - `Neuraxle` [Code Machine Learning Pipelines - The Right Way](https://github.com/Neuraxio/Neuraxle) 39 | - `C++` 40 | - `Caffe` [Deep learning framework by the BVLC](http://caffe.berkeleyvision.org/) :star: 41 | - `clDNN` [Compute Library for Deep Neural Networks](https://github.com/01org/clDNN) 42 | - `CNTK` [The Microsoft Cognitive Toolkit.](https://github.com/Microsoft/CNTK) 43 | - `DeepDetect` [Open Source Deep Learning Server & API](https://deepdetect.com/) 44 | - `DIGITS` [A new system for developing, training and visualizing deep neural networks.](https://developer.nvidia.com/digits) 45 | - `Dlib` [A modern C++ toolkit containing machine learning algorithms and tools for creating complex software in C++.](http://dlib.net/) 46 | - `Dll` [Deep Learning Library](https://github.com/wichtounet/dll) 47 | - `DSSTNE` [An Amazon developed library for building Deep Learning (DL) machine learning (ML) models.](https://github.com/amznlabs/amazon-dsstne) 48 | - `Jik` [Lightweight Deep Learning Framework](https://github.com/oliviersoares/jik) 49 | - `Kann` [A lightweight C library for artificial neural networks](https://github.com/attractivechaos/kann) 50 | - `OpenNN` [OpenNN - Open Neural Networks Library](https://github.com/Artelnics/OpenNN) 51 | - `PaddlePaddle` [An easy-to-use, efficient, flexible and scalable deep learning platform.](http://www.paddlepaddle.org/) 52 | - `MaTEx` [Machine Learning Toolkit for Extreme Scale](https://github.com/matex-org/matex) 53 | - `MRPT` [The Mobile Robot Programming Toolkit (MRPT)](https://github.com/MRPT/mrpt) 54 | - `MXNet` [A flexible and efficient deep learning library for heterogeneous distributed systems with multi-language support](http://mxnet.io/) :star: 55 | - `MinPy` [Providing a high performing and flexible deep learning platform, by prototyping a pure NumPy interface above MXNet backend.](https://github.com/dmlc/minpy) 56 | - `Neural Designer` [Neural Designer is the most advanced analytics software](https://www.neuraldesigner.com/) 57 | - `NNabla` [NNabla is a deep learning framework that is intended to be used for research, development and production](https://nnabla.org/) 58 | - `NVIDIA TensorRT` [High performance deep learning inference for production deployment](https://developer.nvidia.com/tensorrt) 59 | - `Singa` [An Apache Incubating project for developing an open source deep learning library.](http://singa.incubator.apache.org/en/index.html) 60 | - `Tiny-dnn` [A C++11 implementation of deep learning.](https://github.com/tiny-dnn/tiny-dnn) 61 | - `Java` 62 | - `Stanford CoreNLP` [A Java suite of core NLP tools.](https://github.com/stanfordnlp/CoreNLP) 63 | - `Deeplearning4J` [Neural Net Platform.](https://github.com/deeplearning4j/deeplearning4j) 64 | - `NeuralNetworks` [This is a Java implementation of some of the algorithms for training deep neural networks.](https://github.com/ivan-vasilev/neuralnetworks) 65 | - `NewralNet` [A lightweight, easy to use and open source Java library for experimenting with feed-forward neural nets and deep learning.](https://gitlab.com/flimmerkiste/NewralNet/tree/master) 66 | - `Scala` 67 | - `BigDL` [Distributed Deep learning on Apache Spark.](https://github.com/intel-analytics/BigDL) 68 | - `Julia` 69 | - `Knet` [The Koç University deep learning framework implemented in Julia.](https://github.com/denizyuret/Knet.jl) 70 | - `Mocha` [A Deep Learning framework for Julia, inspired by the C++ framework Caffe.](https://github.com/pluskid/Mocha.jl) 71 | - `Js` 72 | - `Brain` [Brain.js is a library of JavaScript neural networks.](https://github.com/harthur-org/brain.js) 73 | - `Deeplearnjs` [A hardware-accelerated machine intelligence library for the web.](https://github.com/PAIR-code/deeplearnjs) 74 | - `Keras-js` [Run Keras models (tensorflow backend) in the browser, with GPU support.](https://github.com/transcranial/keras-js) 75 | - `Neataptic` [Flexible neural network library with advanced neuroevolution](https://github.com/wagenaartje/neataptic) 76 | - `Neurojs` [A javascript deep learning and reinforcement learning library.](https://github.com/janhuenermann/neurojs) 77 | - `Synapses` [A lightweight library for neural networks that runs anywhere.](https://github.com/mrdimosthenis/Synapses) 78 | - `Matlab` 79 | - `MatConvNet` [CNNs for MATLAB](http://www.vlfeat.org/matconvnet/) 80 | - `DLMatFramework` [Deep Learning Matlab Framework](https://github.com/leonardoaraujosantos/DLMatFramework) 81 | - `Lua` 82 | - `OpenNMT` [Open-Source Neural Machine Translation](https://github.com/opennmt/opennmt) 83 | - `Torch7` [Deep learning library in Lua, used by Facebook and Google Deepmind](http://torch.ch/) :star: 84 | - `Php` 85 | - `PHP-ML` [Machine Learning library for PHP](https://github.com/php-ai/php-ml) 86 | - `Dub` 87 | - `Vectorflow` [A minimalist neural network library optimized for sparse data and single machine environments.](https://github.com/Netflix/vectorflow) 88 | 89 | -------------------------------------------------------------------------------- /tutorials.md: -------------------------------------------------------------------------------- 1 | # Tutorials 2 | 3 | * [UFLDL Tutorial 1](http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial) 4 | * [UFLDL Tutorial 2](http://ufldl.stanford.edu/tutorial/supervised/LinearRegression/) 5 | * [Deep Learning for NLP (without Magic)](http://www.socher.org/index.php/DeepLearningTutorial/DeepLearningTutorial) 6 | * [A Deep Learning Tutorial: From Perceptrons to Deep Networks](http://www.toptal.com/machine-learning/an-introduction-to-deep-learning-from-perceptrons-to-deep-networks) 7 | * [Deep Learning from the Bottom up](http://www.metacademy.org/roadmaps/rgrosse/deep_learning) 8 | * [Theano Tutorial](http://deeplearning.net/tutorial/deeplearning.pdf) 9 | * [Neural Networks for Matlab](http://uk.mathworks.com/help/pdf_doc/nnet/nnet_ug.pdf) 10 | * [Using convolutional neural nets to detect facial keypoints tutorial](http://danielnouri.org/notes/2014/12/17/using-convolutional-neural-nets-to-detect-facial-keypoints-tutorial/) 11 | * [Torch7 Tutorials](https://github.com/clementfarabet/ipam-tutorials/tree/master/th_tutorials) 12 | * [The Best Machine Learning Tutorials On The Web](https://github.com/josephmisiti/machine-learning-module) 13 | * [VGG Convolutional Neural Networks Practical](http://www.robots.ox.ac.uk/~vgg/practicals/cnn/index.html) 14 | * [TensorFlow tutorials](https://github.com/nlintz/TensorFlow-Tutorials) 15 | * [More TensorFlow tutorials](https://github.com/pkmital/tensorflow_tutorials) 16 | * [TensorFlow Python Notebooks](https://github.com/aymericdamien/TensorFlow-Examples) 17 | * [Keras and Lasagne Deep Learning Tutorials](https://github.com/Vict0rSch/deep_learning) 18 | * [Classification on raw time series in TensorFlow with a LSTM RNN](https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition) 19 | * [Using convolutional neural nets to detect facial keypoints tutorial](http://danielnouri.org/notes/2014/12/17/using-convolutional-neural-nets-to-detect-facial-keypoints-tutorial/) 20 | * [TensorFlow-World](https://github.com/astorfi/TensorFlow-World) 21 | --------------------------------------------------------------------------------