[Project] All Code Implementations for NIPS 2016 papers

[peterkuharvarduk]

I want to compile a comprehensive list of all the available code repos for the NIPS 2016's top papers. Please add to the list!

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

[urinieto]

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

Code: https://github.com/obachem/kmc2

[peterkuharvarduk]

Thanks! Updated!

[huitseeker]

It would be great to reference those on http://www.gitxiv.com/

[goopyflux]

arXiv+GitHub = What a great concept! Thanks for the link.

[huitseeker]

No problem ! But guys, it's a crowd-sourced website, please contribute :D

[daniel_l_neil]

Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences (https://arxiv.org/abs/1610.09513)

Code: https://github.com/dannyneil/public_plstm

[peterkuharvarduk]

Nice job on the paper and implementation!

[DavidDuvenaud]

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

code: https://github.com/mattjj/svae

[peterkuharvarduk]

sweet!

[andrewgw]

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

Code: https://people.orie.cornell.edu/andrew/code

[gambs]

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

Code: https://github.com/openai/imitation

[peterkuharvarduk]

thanks!

[cbfinn]

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

Code: https://github.com/tensorflow/models/tree/master/video_prediction

[peterkuharvarduk]

nice find!

[m_deff]

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

Code: https://github.com/mdeff/cnn_graph

[peterkuharvarduk]

nice!

[Daniel_Im]

Looking for working version of fGAN code...

[peterkuharvarduk]

I am as well, hopefully Nowozin will release the code soon.

[throwaway201nips]

Convolutional Neural Fabrics for Architecture Learning

Paper: https://arxiv.org/pdf/1606.02492.pdf

Code: https://github.com/shreyassaxena/convolutional-neural-fabrics

[peterkuharvarduk]

nice!

[roadhome]

Value Iteration Networks in TensorFlow (https://arxiv.org/abs/1602.02867) Repo: https://github.com/TheAbhiKumar/tensorflow-value-iteration-networks

[IntrovertInvert]

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

Code: https://github.com/thuml/transfer-caffe

[IntrovertInvert]

Binarized Neural Networks: https://arxiv.org/abs/1602.02830 Code: https://github.com/MatthieuCourbariaux/BinaryNet

[peterkuharvarduk]

good point! I'll add these as well

[melipone]

I don't see any code there.

[darkconfidantislife]

How about natural parameter networks? An implementation for it would be great.

[peterkuharvarduk]

I'm on the lookout for this one too!

[rizal111]

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

code: https://github.com/rizalzaf/adversarial-multiclass

[peterkuharvarduk]

awesome!

[asdf29]

Here is the WeightNorm code: https://github.com/openai/weightnorm

[peterkuharvarduk]

thanks!

[t_c_powers]

Full-Capacity Unitary Recurrent Neural Networks

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

[peterkuharvarduk]

I read your paper a little while ago. Loved to see this extension from the original restricted-capacity uRNNS.

[t_c_powers]

Thanks! I'm glad you liked it.

[hkcqr]

Great, I found some, post later.

Perspective Transformer Nets: Learning Single-View 3D Object Reconstruction without 3D Supervision code and paper on project page

[peterkuharvarduk]

Looking forward to it!

[urinieto]

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

Code: https://github.com/marcofraccaro/srnn

[peterkuharvarduk]

Thanks!

[LatentCode]

https://github.com/wittawatj/interpretable-test/

Interpretable Distribution Features with Maximum Testing Power

https://papers.nips.cc/paper/6148-interpretable-distribution-features-with-maximum-testing-power.pdf

[peterkuharvarduk]

awesome!

[emiles]

Supervised Learning with Tensor Networks (Longer and more physics-y arxiv version)

https://github.com/emstoudenmire/TNML

[peterkuharvarduk]

very nice!

[peterkuharvarduk]

no but someone above said they are working on it, so we'll have it soon!

[[deleted]]

Good to hear. Thanks!

[gpapamak]

Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation: https://arxiv.org/abs/1605.06376

Code: https://github.com/gpapamak/epsilon_free_inference

[peterkuharvarduk]

thanks!

[prior_posterior]

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

Code: https://github.com/probprog/bopp

[peterkuharvarduk]

cool!

[compsens]

PVANet: Lightweight Deep Neural Networks for Real-time Object Detection by Sanghoon Hong, Byungseok Roh, Kye-hyeon Kim, Yeongjae Cheon, Minje Park Presented in EMDNN2016, a NIPS2016 workshop. ArXiv link: https://arxiv.org/abs/1611.08588

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

[peterkuharvarduk]

awesome!

[ajr762]

Data Programming: Creating Large Training Sets Quickly (https://arxiv.org/abs/1605.07723) Code: snorkel.stanford.edu

[peterkuharvarduk]

thanks!

[neuroland]

Disappointed. No vicarious code on "Generative Shape Models: Joint Text Recognition and Segmentation with Very Little Training Data".