MuProp: Unbiased Backpropagation for Stochastic Neural Networks

16 November 2015

Papers citing "MuProp: Unbiased Backpropagation for Stochastic Neural Networks"

41 / 41 papers shown

Title
Learning to Inference Adaptively for Multimodal Large Language Models Zhuoyan Xu Khoi Duc Nguyen Preeti Mukherjee Saurabh Bagchi Somali Chaterji Yingyu Liang Yin Li LRM 52 1 0 13 Mar 2025
Bridging Discrete and Backpropagation: Straight-Through and Beyond Liyuan Liu Chengyu Dong Xiaodong Liu Bin-Xia Yu Jianfeng Gao BDL 26 20 0 17 Apr 2023
Machine learning using magnetic stochastic synapses Matthew O. A. Ellis A. Welbourne Stephan J. Kyle P. Fry D. Allwood T. Hayward Eleni Vasilaki 24 5 0 03 Mar 2023
AskewSGD : An Annealed interval-constrained Optimisation method to train Quantized Neural Networks Louis Leconte S. Schechtman Eric Moulines 29 4 0 07 Nov 2022
LARO: Learned Acquisition and Reconstruction Optimization to accelerate Quantitative Susceptibility Mapping Jinwei Zhang P. Spincemaille Hang Zhang Thanh D. Nguyen Chao Li Jiahao Nick Li I. Kovanlikaya M. Sabuncu Yi Wang 26 9 0 01 Nov 2022
Gradient Estimation with Discrete Stein Operators Jiaxin Shi Yuhao Zhou Jessica Hwang Michalis K. Titsias Lester W. Mackey 33 22 0 19 Feb 2022
Recall@k Surrogate Loss with Large Batches and Similarity Mixup Yash J. Patel Giorgos Tolias Jirí Matas VLM 44 54 0 25 Aug 2021
A Unified Deep Model of Learning from both Data and Queries for Cardinality Estimation Peizhi Wu Gao Cong OOD 16 64 0 26 Jul 2021
Debiasing a First-order Heuristic for Approximate Bi-level Optimization Valerii Likhosherstov Xingyou Song K. Choromanski Jared Davis Adrian Weller AI4CE 19 5 0 04 Jun 2021
Deep Learning-based Resource Allocation For Device-to-Device Communication Woongsup Lee R. Schober 17 38 0 25 Nov 2020
Sample Efficient Reinforcement Learning with REINFORCE Junzi Zhang Jongho Kim Brendan O'Donoghue Stephen P. Boyd 42 101 0 22 Oct 2020
VarGrad: A Low-Variance Gradient Estimator for Variational Inference Lorenz Richter Ayman Boustati Nikolas Nusken Francisco J. R. Ruiz Ömer Deniz Akyildiz DRL 138 48 0 20 Oct 2020
Gradient Estimation with Stochastic Softmax Tricks Max B. Paulus Dami Choi Daniel Tarlow Andreas Krause Chris J. Maddison BDL 36 85 0 15 Jun 2020
UFO-BLO: Unbiased First-Order Bilevel Optimization Valerii Likhosherstov Xingyou Song K. Choromanski Jared Davis Adrian Weller 34 7 0 05 Jun 2020
Path Sample-Analytic Gradient Estimators for Stochastic Binary Networks Alexander Shekhovtsov V. Yanush B. Flach MQ 37 10 0 04 Jun 2020
Joint Stochastic Approximation and Its Application to Learning Discrete Latent Variable Models Zhijian Ou Yunfu Song BDL 32 8 0 28 May 2020
Memristors -- from In-memory computing, Deep Learning Acceleration, Spiking Neural Networks, to the Future of Neuromorphic and Bio-inspired Computing A. Mehonic Abu Sebastian Bipin Rajendran Osvaldo Simeone Eleni Vasilaki A. Kenyon 24 200 0 30 Apr 2020
Estimating Gradients for Discrete Random Variables by Sampling without Replacement W. Kool H. V. Hoof Max Welling BDL 31 49 0 14 Feb 2020
Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax Andres Potapczynski G. Loaiza-Ganem John P. Cunningham 32 29 0 19 Dec 2019
Straight-Through Estimator as Projected Wasserstein Gradient Flow Pengyu Cheng YooJung Choi Yitao Liang Dinghan Shen Ricardo Henao Mathias Niepert 24 14 0 05 Oct 2019
Monte Carlo Gradient Estimation in Machine Learning S. Mohamed Mihaela Rosca Michael Figurnov A. Mnih 45 397 0 25 Jun 2019
ARSM: Augment-REINFORCE-Swap-Merge Estimator for Gradient Backpropagation Through Categorical Variables Mingzhang Yin Yuguang Yue Mingyuan Zhou 16 23 0 04 May 2019
TD-Regularized Actor-Critic Methods Simone Parisi Voot Tangkaratt Jan Peters Mohammad Emtiyaz Khan OffRL 27 32 0 19 Dec 2018
Neural Joint Source-Channel Coding Kristy Choi Kedar Tatwawadi Aditya Grover Tsachy Weissman Stefano Ermon 13 38 0 19 Nov 2018
Bayesian Prediction of Future Street Scenes using Synthetic Likelihoods Apratim Bhattacharyya Mario Fritz Bernt Schiele UQCV 25 46 0 01 Oct 2018
Improved Gradient-Based Optimization Over Discrete Distributions Evgeny Andriyash Arash Vahdat W. Macready 16 9 0 29 Sep 2018
A Review of Learning with Deep Generative Models from Perspective of Graphical Modeling Zhijian Ou 31 16 0 05 Aug 2018
Accurate and Diverse Sampling of Sequences based on a "Best of Many" Sample Objective Apratim Bhattacharyya Bernt Schiele Mario Fritz 28 113 0 20 Jun 2018
Bayesian Prediction of Future Street Scenes through Importance Sampling based Optimization Apratim Bhattacharyya Mario Fritz Bernt Schiele UQCV BDL 24 2 0 18 Jun 2018
Reparameterization Gradient for Non-differentiable Models Wonyeol Lee Hangyeol Yu Hongseok Yang DRL 25 30 0 01 Jun 2018
DVAE++: Discrete Variational Autoencoders with Overlapping Transformations Arash Vahdat W. Macready Zhengbing Bian Amir Khoshaman Evgeny Andriyash 37 75 0 14 Feb 2018
Stochastic Sequential Neural Networks with Structured Inference Hao Liu Haoli Bai Lirong He Zenglin Xu BDL 23 3 0 24 May 2017
Data-efficient Deep Reinforcement Learning for Dexterous Manipulation I. Popov N. Heess Timothy Lillicrap Roland Hafner Gabriel Barth-Maron Matej Vecerík Thomas Lampe Yuval Tassa Tom Erez Martin Riedmiller OffRL 17 263 0 10 Apr 2017
REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models George Tucker A. Mnih Chris J. Maddison John Lawson Jascha Narain Sohl-Dickstein BDL 47 282 0 21 Mar 2017
Boundary-Seeking Generative Adversarial Networks R. Devon Hjelm Athul Paul Jacob Tong Che Adam Trischler Kyunghyun Cho Yoshua Bengio GAN 23 170 0 27 Feb 2017
Stochastic Generative Hashing Bo Dai Ruiqi Guo Sanjiv Kumar Niao He Le Song TPM 35 106 0 11 Jan 2017
Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic S. Gu Timothy Lillicrap Zoubin Ghahramani Richard Turner Sergey Levine OffRL BDL 24 343 0 07 Nov 2016
The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables Chris J. Maddison A. Mnih Yee Whye Teh BDL 24 2,508 0 02 Nov 2016
Deep Amortized Inference for Probabilistic Programs Daniel E. Ritchie Paul Horsfall Noah D. Goodman TPM 24 81 0 18 Oct 2016
Overdispersed Black-Box Variational Inference Francisco J. R. Ruiz Michalis K. Titsias David M. Blei 22 47 0 03 Mar 2016
Variational inference for Monte Carlo objectives A. Mnih Danilo Jimenez Rezende DRL BDL 52 288 0 22 Feb 2016