├── Etc.md ├── Classification.md ├── GAN.md ├── Media.md ├── Rethinking.md ├── Articles.md └── README.md /Etc.md: -------------------------------------------------------------------------------- 1 | 2 | ### Something 3 | 4 | - Learn Machine Learning in Reddit \ 5 | https://www.reddit.com/r/learnmachinelearning 6 | -------------------------------------------------------------------------------- /Classification.md: -------------------------------------------------------------------------------- 1 | ### Classification of Point Clouds 2 | 3 | - [[Link]](https://www.gim-international.com/content/article/object-based-classification-of-point-clouds) 4 | Object-based Classification of Point Clouds - Augmenting 3D Geometry with Semantic Information - \ 5 | GIM International 6 | -------------------------------------------------------------------------------- /GAN.md: -------------------------------------------------------------------------------- 1 | 2 | ### GAN 3 | 4 | - GAN Lab: Understanding Complex Deep Generative Models using Interactive Visual Experimentation \ 5 | https://www.groundai.com/project/gan-lab-understanding-complex-deep-generative-models-using-interactive-visual-experimentation/ 6 | 7 | - Advanced GANs \ 8 | https://ratsgo.github.io/generative%20model/2017/12/21/gans/ 9 | -------------------------------------------------------------------------------- /Media.md: -------------------------------------------------------------------------------- 1 | ### Podcast 2 | 3 | - TWiML & AI (This Week in Machine Learning & AI) \ 4 | https://twimlai.com 5 | 6 | ### Youtube related to Deep Learning 7 | 8 | - 3BLUE1BROWN SERIES S3 \ 9 | 신경망이란 무엇인가? | 1장. 딥러닝에 관하여 \ 10 | https://youtu.be/aircAruvnKk 11 | 12 | 13 | - KoreaUniv DSBA \ 14 | https://www.youtube.com/channel/UCPq01cgCcEwhXl7BvcwIQyg 15 | -------------------------------------------------------------------------------- /Rethinking.md: -------------------------------------------------------------------------------- 1 | 2 | ### Rethinking Existing Deep Learning Techniques 3 | 4 | - Rethinking ImageNet Pre-training [[Paper]](https://arxiv.org/abs/1811.08883) \ 5 | Kaiming He, Ross Girshick, Piotr Dollar \ 6 | 7 | - Measuring the Effects of Data Parallelism on Neural Network Training [[Paper]](https://arxiv.org/abs/1811.03600) \ 8 | Christopher J. Shallue, Jaehoon Lee, Joseph Antognini, Jascha Sohl-Dickstein, Roy Frostig, George E. Dahl \ 9 | 10 | -------------------------------------------------------------------------------- /Articles.md: -------------------------------------------------------------------------------- 1 | 2 | ### GAN (Generative Adversarial Network) 3 | 4 | - GANs from Scratch 1: A deep introduction. With code in PyTorch and TensorFlow \ 5 | https://medium.com/ai-society/gans-from-scratch-1-a-deep-introduction-with-code-in-pytorch-and-tensorflow-cb03cdcdba0f 6 | 7 | ### Data Augmentation 8 | 9 | - Generative Models for Data Augmentation \ 10 | https://towardsdatascience.com/generative-adversarial-networks-for-data-augmentation-experiment-design-2873d586eb59 11 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Deep Learning for Spatio temporal Prediction 2 | 3 | - The Building Blocks of Interpretability 4 | https://distill.pub/2018/building-blocks/ 5 | 6 | - Neural Networks and Deep Learning (free online book) 7 | http://neuralnetworksanddeeplearning.com/index.html 8 | 9 | ## Spatiotemporal Prediction 10 | 11 | ### Theory 12 | 13 | - [NIPS 2015] Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting (Xingjian Shi, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-kin Wong, Wang-chun Woo) 14 | https://arxiv.org/abs/1506.04214 15 | 16 | - [NIPS 2017] Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model 17 | https://arxiv.org/abs/1706.03458 18 | 19 | - [ICML 2017] Deep Forecast: Deep Learning-based Spatio-Temporal Forecasting 20 | https://arxiv.org/abs/1707.08110 21 | 22 | - [ICML 2018] PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning 23 | https://arxiv.org/abs/1804.06300 \ 24 | http://proceedings.mlr.press/v80/wang18b.html \ 25 | [Official CODE] https://github.com/Yunbo426/predrnn-pp 26 | 27 | - [ICDM 2018] Spatio-Temporal Neural Networks for Space-Time Series Forecasting and Relations Discovery 28 | https://arxiv.org/pdf/1804.08562.pdf \ 29 | [Official CODE] https://github.com/edouardelasalles/stnn 30 | 31 | ### Application 32 | 33 | - Deep-STEP: A Deep Learning Approach for Spatiotemporal Prediction of Remote Sensing Data (Monidipa Das, Soumya K. Ghosh) 34 | https://ieeexplore.ieee.org/document/7752890 35 | 36 | - DeepRain: ConvLSTM Network for Precipitation Prediction Using Multichannel Radar Data (Seongchan Kim, Seungkyun Hong, Minsu Joh, Sa-kwang Song) \ 37 | https://arxiv.org/abs/1711.02316 38 | 39 | - [CIKM 2017 Contest] Convolutional LSTM neural network to extrapolate radar images, and predict rainfal \ 40 | https://github.com/TeaPearce/precipitation-prediction-convLSTM-keras 41 | 42 | - [NIPS 2017] PSIque: Next Sequence Prediction of Satellite Images using a Convolutional Sequence-to-Sequence Network 43 | https://dl4physicalsciences.github.io/files/nips_dlps_2017_12.pdf 44 | 45 | - [IWCI 2017] Globenet: Convolutional neural networks for typhoon eye tracking from remote sensing imagery 46 | 47 | - DNN-Based Prediction Model for Spatial-Temporal Data \ 48 | https://www.microsoft.com/en-us/research/wp-content/uploads/2016/09/DeepST-SIGSPATIAL2016.pdf 49 | 50 | - Deep Forecast: Deep Learning-based Spatio-Temporl Forecasting \ 51 | http://roseyu.com/time-series-workshop/submissions/TSW2017_paper_2.pdf 52 | 53 | ## Authors' homepage 54 | - Xingjian Shi (施行健) 55 | https://home.cse.ust.hk/~xshiab/ 56 | - Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model 57 | 58 | ## Abbreviations 59 | - NIPS Neural Information Processing Systems (NIPS) 60 | - ICML International Conference on Machine Learning 61 | - ICDM IEEE International Conference on Data Mining series (ICDM) 62 | - ICWI International Workshop on Climate Informatics (ICWI) 63 | --------------------------------------------------------------------------------