本文為NIPS 2016 top papers的代碼實現(xiàn)匯總,轉自 reddit 帖子。

1. ?Using Fast Weights to Attend to the Recent Past (https://arxiv.org/abs/1610.06258)

   Repo:?https://github.com/ajarai/fast-weights

2. Learning to learn by gradient descent by gradient descent (https://arxiv.org/abs/1606.04474)

Repo:?https://github.com/deepmind/learning-to-learn

3. R-FCN: Object Detection via Region-based Fully Convolutional Networks (https://arxiv.org/abs/1605.06409)

Repo:?https://github.com/Orpine/py-R-FCN

4. Fast and Provably Good Seedings for k-Means (https://las.inf.ethz.ch/files/bachem16fast.pdf).

Repo:?https://github.com/obachem/kmc2

5. How to Train a GAN

Repo:?https://github.com/soumith/ganhacks

6. 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

7. Generative Adversarial Imitation Learning (https://arxiv.org/abs/1606.03476)

Repo:?https://github.com/openai/imitation

8. Adversarial Multiclass Classification: A Risk Minimization Perspective (https://www.cs.uic.edu/~rfathony/pdf/fathony2016adversarial.pdf)

Repo:?https://github.com/rizalzaf/adversarial-multiclass

9. Unsupervised Learning for Physical Interaction through Video Prediction (https://arxiv.org/abs/1605.07157)

Repo:?https://github.com/tensorflow/models/tree/master/video_prediction

10. Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks (https://arxiv.org/abs/1602.07868)

Repo:?https://github.com/openai/weightnorm

11. Full-Capacity Unitary Recurrent Neural Networks (https://arxiv.org/abs/1611.00035)

Repo: Code:?https://github.com/stwisdom/urnn

12. Sequential Neural Models with Stochastic Layers (https://arxiv.org/pdf/1605.07571.pdf)

Repo:?https://github.com/marcofraccaro/srnn

13. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering (https://arxiv.org/abs/1606.09375)

Repo:?https://github.com/mdeff/cnn_graph

14. 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/

15. Composing graphical models with neural networks for structured representations and fast inference (https://arxiv.org/abs/1603.06277)

Repo:?https://github.com/mattjj/svae

16. Supervised Learning with Tensor Networks (https://arxiv.org/abs/1605.05775)

Repo:?https://github.com/emstoudenmire/TNML

17. 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

18. Bayesian Optimization for Probabilistic Programs (http://www.robots.ox.ac.uk/~twgr/assets/pdf/rainforth2016BOPP.pdf)

Repo:?https://github.com/probprog/bopp

19. PVANet: Lightweight Deep Neural Networks for Real-time Object Detection (https://arxiv.org/abs/1611.08588)

Repo:?https://github.com/sanghoon/pva-faster-rcnn

20. Data Programming: Creating Large Training Sets Quickly (https://arxiv.org/abs/1605.07723)

Repo: snorkel.stanford.edu

21. Convolutional Neural Fabrics for Architecture Learning (https://arxiv.org/pdf/1606.02492.pdf)

Repo:?https://github.com/shreyassaxena/convolutional-neural-fabrics

22. Value Iteration Networks in TensorFlow (https://arxiv.org/abs/1602.02867)

Repo:?https://github.com/TheAbhiKumar/tensorflow-value-iteration-networks

23. Stochastic Variational Deep Kernel Learning (https://arxiv.org/abs/1611.00336)

Repo:?https://people.orie.cornell.edu/andrew/code

24. Unsupervised Domain Adaptation with Residual Transfer Networks (https://arxiv.org/abs/1602.04433)

Repo:?https://github.com/thuml/transfer-caffe

25. Binarized Neural Networks (https://arxiv.org/abs/1602.02830)

Repo:?https://github.com/MatthieuCourbariaux/BinaryNet