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/-
Using Fast Weights to Attend to the Recent Past (https://arxiv.org/abs/1610.06258)
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Learning to learn by gradient descent by gradient descent (https://arxiv.org/abs/1606.04474)
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R-FCN: Object Detection via Region-based Fully Convolutional Networks (https://arxiv.org/abs/1605.06409)
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Fast and Provably Good Seedings for k-Means (https://las.inf.ethz.ch/files/bachem16fast.pdf).
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How to Train a GAN
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Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences (https://arxiv.org/abs/1610.09513)
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Generative Adversarial Imitation Learning (https://arxiv.org/abs/1606.03476)
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Adversarial Multiclass Classification: A Risk Minimization Perspective (https://www.cs.uic.edu/~rfathony/pdf/fathony2016adversarial.pdf)
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Unsupervised Learning for Physical Interaction through Video Prediction (https://arxiv.org/abs/1605.07157)
Repo: https://github.com/tensorflow/models/tree/master/video_prediction
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Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks (https://arxiv.org/abs/1602.07868)
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Full-Capacity Unitary Recurrent Neural Networks (https://arxiv.org/abs/1611.00035)
Repo: Code: https://github.com/stwisdom/urnn
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Sequential Neural Models with Stochastic Layers (https://arxiv.org/pdf/1605.07571.pdf)
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Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering (https://arxiv.org/abs/1606.09375)
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Interpretable Distribution Features with Maximum Testing Power (https://papers.nips.cc/paper/6148-interpretable-distribution-features-with-maximum-testing-power.pdf)
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Composing graphical models with neural networks for structured representations and fast inference (https://arxiv.org/abs/1603.06277)
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Supervised Learning with Tensor Networks (https://arxiv.org/abs/1605.05775)
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Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation: (https://arxiv.org/abs/1605.06376)
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Bayesian Optimization for Probabilistic Programs (http://www.robots.ox.ac.uk/~twgr/assets/pdf/rainforth2016BOPP.pdf)
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PVANet: Lightweight Deep Neural Networks for Real-time Object Detection (https://arxiv.org/abs/1611.08588)
Repo: https://github.com/sanghoon/pva-faster-rcnn //caffe
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Data Programming: Creating Large Training Sets Quickly (https://arxiv.org/abs/1605.07723)
Repo: snorkel.stanford.edu
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Convolutional Neural Fabrics for Architecture Learning (https://arxiv.org/pdf/1606.02492.pdf)
Repo: https://github.com/shreyassaxena/convolutional-neural-fabrics
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Value Iteration Networks in TensorFlow (https://arxiv.org/abs/1602.02867)
Repo: https://github.com/TheAbhiKumar/tensorflow-value-iteration-networks
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Stochastic Variational Deep Kernel Learning (https://arxiv.org/abs/1611.00336)
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Unsupervised Domain Adaptation with Residual Transfer Networks (https://arxiv.org/abs/1602.04433)
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Binarized Neural Networks (https://arxiv.org/abs/1602.02830)
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