NIPS 2016 페이퍼가 구현된 레파지토리를 정리하는 레딧 포스트가 있습니다. 지금까지 13개의 깃허브 레파지토리가 정리되어 있습니다. 추가되는 대로 업데이트 하겠습니다. 혹시 이 외에 다른 레파지토리가 있다면 공유 부탁 드립니다.
- Using Fast Weights to Attend to the Recent Past (https://arxiv.org/abs/1610.06258)
Repo: https://github.com/ajarai/fast-weights - Learning to learn by gradient descent by gradient descent (https://arxiv.org/abs/1606.04474)
Repo: https://github.com/deepmind/learning-to-learn - R-FCN: Object Detection via Region-based Fully Convolutional Networks (https://arxiv.org/abs/1605.06409)
Repo: https://github.com/Orpine/py-R-FCN - Fast and Provably Good Seedings for k-Means (https://las.inf.ethz.ch/files/bachem16fast.pdf)
Repo: https://github.com/obachem/kmc2 - How to Train a GAN
Repo: https://github.com/soumith/ganhacks - Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences (https://arxiv.org/abs/1610.09513)
Repo: https://github.com/dannyneil/public_plstm - Generative Adversarial Imitation Learning (https://arxiv.org/abs/1606.03476)
Repo: https://github.com/openai/imitation - Adversarial Multiclass Classification: A Risk Minimization Perspective (https://www.cs.uic.edu/~rfathony/pdf/fathony2016adversarial.pdf)
Repo: https://github.com/rizalzaf/adversarial-multiclass - Unsupervised Learning for Physical Interaction through Video Prediction (https://arxiv.org/abs/1605.07157)
Repo: https://github.com/tensorflow/models/tree/master/video_prediction - Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks (https://arxiv.org/abs/1602.07868)
Repo: https://github.com/openai/weightnorm - Full-Capacity Unitary Recurrent Neural Networks (https://arxiv.org/abs/1611.00035)
Repo: https://github.com/stwisdom/urnn - Sequential Neural Models with Stochastic Layers (https://arxiv.org/pdf/1605.07571.pdf)
Repo: https://github.com/marcofraccaro/srnn - Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering (https://arxiv.org/abs/1606.09375)
Repo: https://github.com/mdeff/cnn_graph - Interpretable Distribution Features with Maximum Testing Power (https://papers.nips.cc/paper/6148-interpretable-distribution-features-with-maximum-testing-power.pdf)
Repo: https://github.com/wittawatj/interpretable-test/ - Composing graphical models with neural networks for structured representations and fast inference (https://arxiv.org/abs/1603.06277)
Repo: https://github.com/mattjj/svae - Supervised Learning with Tensor Networks (https://arxiv.org/abs/1605.05775)
Repo: https://github.com/emstoudenmire/TNML - Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation: (https://arxiv.org/abs/1605.06376)
Repo: https://github.com/gpapamak/epsilon_free_inference - Bayesian Optimization for Probabilistic Programs (http://www.robots.ox.ac.uk/~twgr/assets/pdf/rainforth2016BOPP.pdf)
Repo: https://github.com/probprog/bopp - PVANet: Lightweight Deep Neural Networks for Real-time Object Detection (https://arxiv.org/abs/1611.08588)
Repo: https://github.com/sanghoon/pva-faster-rcnn - Data Programming: Creating Large Training Sets Quickly (https://arxiv.org/abs/1605.07723)
Repo: https://github.com/HazyResearch/snorkel - Convolutional Neural Fabrics for Architecture Learning (https://arxiv.org/pdf/1606.02492.pdf)
Repo: https://github.com/shreyassaxena/convolutional-neural-fabrics - Value Iteration Networks in TensorFlow (https://arxiv.org/abs/1602.02867)
Repo: https://github.com/TheAbhiKumar/tensorflow-value-iteration-networks
가 0으로(vector of zeros) 초기화된다고 쓰여진 것은 오타로 1로 초기화 되어야 한다고 합니다.
텐서 플로우 블로그 (Tensor ≈ Blog) 