Techniques for Learning Binary Stochastic Feedforward Neural Networks

11 June 2014

Papers citing "Techniques for Learning Binary Stochastic Feedforward Neural Networks"

23 / 23 papers shown

Title
AskewSGD : An Annealed interval-constrained Optimisation method to train Quantized Neural Networks Louis Leconte S. Schechtman Eric Moulines 27 4 0 07 Nov 2022
Learning to segment from object sizes Denis Baruvcić Czech Republic SSeg 11 1 0 01 Jul 2022
Modelling Latent Translations for Cross-Lingual Transfer E. Ponti Julia Kreutzer Ivan Vulić Siva Reddy 17 18 0 23 Jul 2021
Fitting summary statistics of neural data with a differentiable spiking network simulator G. Bellec Shuqi Wang Alireza Modirshanechi Johanni Brea W. Gerstner 26 11 0 18 Jun 2021
Path Sample-Analytic Gradient Estimators for Stochastic Binary Networks Alexander Shekhovtsov V. Yanush B. Flach MQ 29 10 0 04 Jun 2020
Joint Stochastic Approximation and Its Application to Learning Discrete Latent Variable Models Zhijian Ou Yunfu Song BDL 24 8 0 28 May 2020
Image compression optimized for 3D reconstruction by utilizing deep neural networks Alexander M. Golts Y. Schechner 3DV 22 12 0 27 Mar 2020
Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax Andres Potapczynski G. Loaiza-Ganem John P. Cunningham 27 29 0 19 Dec 2019
Learned Variable-Rate Image Compression with Residual Divisive Normalization Mohammad Akbari Jie Liang Jingning Han Chengjie Tu 10 25 0 11 Dec 2019
Stochastic Feedforward Neural Networks: Universal Approximation Thomas Merkh Guido Montúfar 17 8 0 22 Oct 2019
PixelVAE++: Improved PixelVAE with Discrete Prior Hossein Sadeghi Evgeny Andriyash W. Vinci L. Buffoni Mohammad H. Amin BDL DRL 19 33 0 26 Aug 2019
Accurate and Diverse Sampling of Sequences based on a "Best of Many" Sample Objective Apratim Bhattacharyya Bernt Schiele Mario Fritz 14 110 0 20 Jun 2018
Deep Learning for Joint Source-Channel Coding of Text Nariman Farsad Milind Rao Andrea J. Goldsmith 15 341 0 19 Feb 2018
Stochastic Generative Hashing Bo Dai Ruiqi Guo Sanjiv Kumar Niao He Le Song TPM 27 106 0 11 Jan 2017
The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables Chris J. Maddison A. Mnih Yee Whye Teh BDL 16 2,504 0 02 Nov 2016
Discrete Variational Autoencoders J. Rolfe BDL DRL 27 254 0 07 Sep 2016
Variational inference for Monte Carlo objectives A. Mnih Danilo Jimenez Rezende DRL BDL 24 288 0 22 Feb 2016
Generating Sentences from a Continuous Space Samuel R. Bowman Luke Vilnis Oriol Vinyals Andrew M. Dai Rafal Jozefowicz Samy Bengio DRL 20 2,340 0 19 Nov 2015
Variable Rate Image Compression with Recurrent Neural Networks G. Toderici Sean M. O'Malley S. Hwang Damien Vincent David C. Minnen S. Baluja Michele Covell Rahul Sukthankar 11 667 0 19 Nov 2015
Predicting distributions with Linearizing Belief Networks Yann N. Dauphin David Grangier 18 18 0 17 Nov 2015
Describing Multimedia Content using Attention-based Encoder--Decoder Networks Kyunghyun Cho Aaron Courville Yoshua Bengio 32 411 0 04 Jul 2015
Difference Target Propagation Dong-Hyun Lee Saizheng Zhang Asja Fischer Yoshua Bengio AAML 16 346 0 23 Dec 2014
Improving neural networks by preventing co-adaptation of feature detectors Geoffrey E. Hinton Nitish Srivastava A. Krizhevsky Ilya Sutskever Ruslan Salakhutdinov VLM 266 7,634 0 03 Jul 2012