NIPS 2106 优秀论文和代码下载地址集锦--持续更新

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    更新1     各种深度学习模型介绍  :         http://blog.paralleldots.com/technology/deep-learning/must-read-path-breaking-papers-about-image-classification/

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

    Repohttps://github.com/ajarai/fast-weights

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

    Repohttps://github.com/deepmind/learning-to-learn

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

    Repohttps://github.com/Orpine/py-R-FCN

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

    Repohttps://github.com/obachem/kmc2

  5. How to Train a GAN

    Repohttps://github.com/soumith/ganhacks

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

    Repohttps://github.com/dannyneil/public_plstm

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

    Repohttps://github.com/openai/imitation

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

    Repohttps://github.com/rizalzaf/adversarial-multiclass

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

    Repohttps://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)

    Repohttps://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)

    Repohttps://github.com/marcofraccaro/srnn

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

    Repohttps://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)

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

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

    Repohttps://github.com/mattjj/svae

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

    Repohttps://github.com/emstoudenmire/TNML

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

    Repohttps://github.com/gpapamak/epsilon_free_inference

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

    Repohttps://github.com/probprog/bopp

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

    Repohttps://github.com/sanghoon/pva-faster-rcnn   //caffe

  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)

    Repohttps://github.com/shreyassaxena/convolutional-neural-fabrics

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

    Repohttps://github.com/TheAbhiKumar/tensorflow-value-iteration-networks

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

    Repohttps://people.orie.cornell.edu/andrew/code

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

    Repohttps://github.com/thuml/transfer-caffe

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

    Repohttps://github.com/MatthieuCourbariaux/BinaryNet

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