Estimating or Propagating Gradients Through Stochastic Neurons for
Conditional Computation

Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

15 August 2013

Nicholas Léonard

Aaron Courville

Papers citing "Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation"

19 / 1,869 papers shown

Title
Adversarially Learned Inference Vincent Dumoulin Ishmael Belghazi Ben Poole Olivier Mastropietro Alex Lamb Martín Arjovsky Aaron Courville GAN 20 1,310 0 02 Jun 2016
Noisy Activation Functions Çağlar Gülçehre Marcin Moczulski Misha Denil Yoshua Bengio 9 283 0 01 Mar 2016
Natural Language Understanding with Distributed Representation Kyunghyun Cho GNN BDL 21 55 0 24 Nov 2015
Dynamic Capacity Networks Amjad Almahairi Nicolas Ballas Tim Cooijmans Yin Zheng Hugo Larochelle Aaron Courville 30 96 0 24 Nov 2015
Compressing Word Embeddings Martin Andrews 14 20 0 19 Nov 2015
Iterative Refinement of the Approximate Posterior for Directed Belief Networks Devon Hjelm Kyunghyun Cho Junyoung Chung R. Salakhutdinov Vince D. Calhoun Nebojsa Jojic BDL 4 1 0 19 Nov 2015
Conditional Computation in Neural Networks for faster models Emmanuel Bengio Pierre-Luc Bacon Joelle Pineau Doina Precup AI4CE 23 315 0 19 Nov 2015
Predicting distributions with Linearizing Belief Networks Yann N. Dauphin David Grangier 26 18 0 17 Nov 2015
MuProp: Unbiased Backpropagation for Stochastic Neural Networks S. Gu Sergey Levine Ilya Sutskever A. Mnih BDL 8 143 0 16 Nov 2015
Describing Multimedia Content using Attention-based Encoder--Decoder Networks Kyunghyun Cho Aaron Courville Yoshua Bengio 32 411 0 04 Jul 2015
Gradient Estimation Using Stochastic Computation Graphs John Schulman N. Heess T. Weber Pieter Abbeel OffRL 19 389 0 17 Jun 2015
Difference Target Propagation Dong-Hyun Lee Saizheng Zhang Asja Fischer Yoshua Bengio AAML 25 346 0 23 Dec 2014
Deep Directed Generative Autoencoders Sherjil Ozair Yoshua Bengio DRL 24 18 0 02 Oct 2014
Exponentially Increasing the Capacity-to-Computation Ratio for Conditional Computation in Deep Learning Kyunghyun Cho Yoshua Bengio 42 38 0 28 Jun 2014
Techniques for Learning Binary Stochastic Feedforward Neural Networks T. Raiko Mathias Berglund Guillaume Alain Laurent Dinh BDL 59 126 0 11 Jun 2014
Low-Rank Approximations for Conditional Feedforward Computation in Deep Neural Networks Andrew S. Davis I. Arel 51 78 0 16 Dec 2013
Learning Factored Representations in a Deep Mixture of Experts David Eigen MarcÁurelio Ranzato Ilya Sutskever MoE 42 367 0 16 Dec 2013
Deep AutoRegressive Networks Karol Gregor Ivo Danihelka A. Mnih Charles Blundell Daan Wierstra AI4TS BDL 24 278 0 31 Oct 2013
Improving neural networks by preventing co-adaptation of feature detectors Geoffrey E. Hinton Nitish Srivastava A. Krizhevsky Ilya Sutskever Ruslan Salakhutdinov VLM 266 7,638 0 03 Jul 2012