22 implementations of #NIPS2016 papers

On Reddit, peterkuharvarduk decided to compile all the implementations available from NIPS2016. I am glad the word implementation is used for this as it permits a faster search. Props to peterkuharvarduk and the contributors for making this list available (I also added that just came out). Let me re-emphasize that GitXiv, one of the most awesomest site on the interwebs already has a few of them.

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
    

 

Value Iteration Networks, Aviv Tamar, Yi Wu, Garrett Thomas, Sergey Levine, Pieter Abbeel
repo: TensorFlow implementation, Aviv Tamar's (author) original implementation in Theano
 
 

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