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资料|NIPS 2016论文实现汇总

发布日期:2020-05-01 21:12:49 作者:管理员 阅读:127

本文为NIPS 2016 top papers的代码实现汇总,转自 reddit 帖子。1.Using Fast Weights to Attend to the Recent Past (https://arxiv.org/abs/161

本文为NIPS 2016 top papers的代码实现汇总,转自 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:  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


原文链接:https://www.reddit.com/r/MachineLearning/comments/5hwqeb/project_all_code_implementations_for_nips_2016/



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