├── .gitignore ├── Application └── A Neural Algorithm of Artistic Style.pdf ├── Deep Learning ├── An overview on data representation learning: From traditional.pdf ├── Computer Vision │ ├── Aggregated Residual Transformations for Deep Neural Networks.pdf │ ├── Deep Networks with Stochastic Depth.pdf │ ├── Deep Residual Learning for Image Recognition.pdf │ ├── Delving Deep into Rectifiers Surpassing Human Level Performance on ImageNet Classification.pdf │ ├── Densely Connected Convolutional Networks.pdf │ ├── Going deeper with convolutions.pdf │ ├── Identity Mappings in Deep Residual Networks.pdf │ ├── ImageNet classification with deep convolutional neural networks.pdf │ ├── Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning.pdf │ ├── Maxout Networks.pdf │ ├── Network In Network.pdf │ ├── Residual Networks of Residual Networks Multilevel Residual Networks.pdf │ ├── Rethinking the Inception Architecture for Computer Vision.pdf │ ├── Training Very Deep Networks.pdf │ ├── Very deep convolutional networks for large-scale image recognition.pdf │ ├── What Do We Understand About Convolutional Networks.pdf │ └── Wide Residual Networks.pdf ├── Dropout A Simple Way to Prevent Neural Networks from Overfitting.pdf ├── Dropout: A Simple Way to Prevent Neural Networks from　Overfitting.pdf ├── Improving neural networks by preventing co-adaptation of feature detectors.pdf ├── Natural Language Processing │ ├── A Primer on Neural Network Models for Natural Language Processing.pdf │ ├── Applications of Deep Learning to Sentiment Analysis.pdf │ ├── Attention │ │ ├── Attention Is All You Need.pdf │ │ ├── Attention and Augmented Recurrent Neural Networks — Distill.pdf │ │ ├── Convolutional Sequence to Sequence Learning.pdf │ │ ├── Neural Machine Translation by Jointly Learning to Align and Translate.pdf │ │ └── Sequence to Sequence Learning with Neural Networks.pdf │ ├── Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling.pdf │ ├── Language Modeling & Generating Text │ │ ├── Extensions of Recurrent neural network based language model.pdf │ │ ├── Generating Sequences With Recurrent Neural Networks.pdf │ │ ├── Generating Text with Recurrent Neural Networks.pdf │ │ ├── Language Modeling with Gated Convolutional Networks.pdf │ │ └── Recurrent neural network based language model.pdf │ ├── Machine Comprehension │ │ ├── Coarse-to-Fine Question Answering for Long Documents.pdf │ │ ├── Neural Question Answering at BioASQ 5B.pdf │ │ ├── PROGRESS AND FUTURE PROSPECTS OF MACHINE READING COMPREHENSION.pdf │ │ └── Question Answering from Unstructured Text by Retrieval and Comprehension.pdf │ ├── Machine Translation │ │ ├── Effective Approaches to Attention-based Neural Machine Translation.pdf │ │ ├── Massive Exploration of Neural Machine Translation Architectures.pdf │ │ └── Neural Machine Translation by Jointly Learning to Align and Translate.pdf │ ├── NLI and Text Matching │ │ ├── A Decomposable Attention Model for Natural Language Inference.pdf │ │ ├── ABCNN Attention-Based Convolutional Neural Network for Modeling Sentence Pairs.pdf │ │ ├── Bilateral Multi-Perspective Matching for Natural Language Sentences.pdf │ │ ├── Enhanced LSTM for Natural Language Inference.pdf │ │ ├── Natural Language Inference over Interaction Space.pdf │ │ ├── Natural Language Inference, Sentence representation and Attention Mechanism.pdf │ │ ├── Semantic Sentence Matching with Densely-connected Recurrent and Co-attentive Information.pdf │ │ └── Siamese Recurrent Architectures for Learning Sentence Similarity.pdf │ ├── Preprocessing Techniques for Text Mining - An Overview.pdf │ ├── Question Answering │ │ └── Reading Wikipedia to Answer Open-Domain Questions.pdf │ ├── Recurrent Convolutional Neural Networks for Scene Parsing.pdf │ ├── Recurrent Neural Networks │ │ ├── A Clockwork RNN.pdf │ │ ├── A Critical Review of Recurrent Neural Networks for Sequence Learning.pdf │ │ ├── Bidirectional Recurrent Neural Networks.pdf │ │ ├── Deep Gate Recurrent Neural Network.pdf │ │ ├── Gated Feedback Recurrent Neural Networks.pdf │ │ ├── How to Construct Deep Recurrent Neural Networks.pdf │ │ ├── Recurrent Neural Network Regularization.pdf │ │ └── Speech Recognition with Deep Recurrent Neural Networks.pdf │ ├── Text Classification │ │ ├── A Sensitivity Analysis of (and Practitioners’ Guide to) Convolutional Neural Networks for Sentence Classification.pdf │ │ ├── ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs.pdf │ │ ├── Bag of Tricks for Efficient Text Classification.pdf │ │ ├── Baselines and Bigrams: Simple, Good Sentiment and Topic Classification.pdf │ │ ├── Convolutional Neural Networks for Sentence Classification.pdf │ │ ├── Hierarchical Attention Networks for Document Classification.pdf │ │ ├── Multilingual Hierarchical Attention Networks for Document Classification.pdf │ │ ├── PPT-Recurrent Convolutional Neural Networks for Text Classification.pdf │ │ ├── Recurrent Convolutional Neural Networks for Text Classification.pdf │ │ └── Semantic Clustering and Convolutional Neural Network for Short Text Categorization.pdf │ └── Word Embedding │ │ ├── A Structured Self-attentive Sentence Embedding.pdf │ │ ├── Baseline Needs More Love On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms.pdf │ │ ├── Deep contextualized word representations.pdf │ │ ├── Distributed representations of words and phrases and their compositionality.pdf │ │ ├── Efficient Estimation of Word Representations in vector space.pdf │ │ └── Enriching Word Vectors with Subword Information.pdf ├── On Optimization Methods for Deep Learning.pdf ├── Searching for Activation Functions 1710.pdf ├── Understanding Deep Image Representations by Inverting Them.pdf ├── Understanding the difficulty of training deep feedforward neural networks.pdf └── Visualizing and Understanding Convolutional Networks.pdf ├── Machine Learning ├── A Few Useful Things to Know about Machine Learning.pdf ├── A method for feature selection based on the correlation analysis.pdf ├── An Analysis of Feature Selection Techniques.pdf ├── Distance-based features in pattern classification.pdf ├── Dual Supervised Learning.pdf └── Learning from Imbalanced Data.pdf └── README.md /.gitignore: -------------------------------------------------------------------------------- 1 | # some ignore files 2 | .idea 3 | logs 4 | git_push.sh 5 | .DS_Store 6 | -------------------------------------------------------------------------------- /Application/A Neural Algorithm of Artistic Style.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SunnyMarkLiu/MachineLearning-DeepLearning-Papers/HEAD/Application/A Neural Algorithm of Artistic Style.pdf -------------------------------------------------------------------------------- /Deep Learning/An overview on data representation learning: From traditional.pdf: 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