REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models

21 March 2017

Jascha Narain Sohl-Dickstein

BDL

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Papers citing "REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models"

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Data augmentation with automated machine learning: approaches and performance comparison with classical data augmentation methods A. Mumuni F. Mumuni 65 5 0 13 Mar 2024
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Efficient Learning of Discrete-Continuous Computation Graphs David Friede Mathias Niepert 13 3 0 26 Jul 2023
Bridging Discrete and Backpropagation: Straight-Through and Beyond Liyuan Liu Chengyu Dong Xiaodong Liu Bin-Xia Yu Jianfeng Gao BDL 20 20 0 17 Apr 2023
Learning Sparsity of Representations with Discrete Latent Variables Zhao Xu Daniel Oñoro-Rubio G. Serra Mathias Niepert 13 0 0 03 Apr 2023
Efficient Attention via Control Variates Lin Zheng Jianbo Yuan Chong-Jun Wang Lingpeng Kong 31 18 0 09 Feb 2023
Automatic Differentiation of Programs with Discrete Randomness Gaurav Arya Moritz Schauer Frank Schafer Chris Rackauckas 23 34 0 16 Oct 2022
SIMPLE: A Gradient Estimator for $k$ -Subset Sampling Kareem Ahmed Zhe Zeng Mathias Niepert Guy Van den Broeck BDL 42 24 0 04 Oct 2022
GFlowNets and variational inference Nikolay Malkin Salem Lahlou T. Deleu Xu Ji J. E. Hu Katie Everett Dinghuai Zhang Yoshua Bengio BDL 134 77 0 02 Oct 2022
Variational Open-Domain Question Answering Valentin Liévin Andreas Geert Motzfeldt Ida Riis Jensen Ole Winther OOD BDL 36 8 0 23 Sep 2022
Stochastic gradient descent with gradient estimator for categorical features Paul Peseux Maxime Bérar Thierry Paquet Victor Nicollet 25 0 0 08 Sep 2022
Latent Variable Modelling Using Variational Autoencoders: A survey Vasanth Kalingeri CML DRL 23 2 0 20 Jun 2022
Sparse Graph Learning from Spatiotemporal Time Series Andrea Cini Daniele Zambon Cesare Alippi CML AI4TS 40 18 0 26 May 2022
A Review of the Gumbel-max Trick and its Extensions for Discrete Stochasticity in Machine Learning Iris A. M. Huijben W. Kool Max B. Paulus Ruud J. G. van Sloun 26 93 0 04 Oct 2021
Differentiable Subset Pruning of Transformer Heads Jiaoda Li Ryan Cotterell Mrinmaya Sachan 37 53 0 10 Aug 2021
Fitting summary statistics of neural data with a differentiable spiking network simulator G. Bellec Shuqi Wang Alireza Modirshanechi Johanni Brea W. Gerstner 34 11 0 18 Jun 2021
RDA: Robust Domain Adaptation via Fourier Adversarial Attacking Jiaxing Huang Dayan Guan Aoran Xiao Shijian Lu AAML 35 76 0 05 Jun 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
Storchastic: A Framework for General Stochastic Automatic Differentiation Emile van Krieken Jakub M. Tomczak A. T. Teije ODL OffRL 25 15 0 01 Apr 2021
Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design Adam Foster Desi R. Ivanova Ilyas Malik Tom Rainforth 28 78 0 03 Mar 2021
Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations N. Jethani Mukund Sudarshan Yindalon Aphinyanagphongs Rajesh Ranganath FAtt 82 70 0 02 Mar 2021
Mind Mappings: Enabling Efficient Algorithm-Accelerator Mapping Space Search Kartik Hegde Po-An Tsai Sitao Huang Vikas Chandra A. Parashar Christopher W. Fletcher 26 90 0 02 Mar 2021
Probabilistic Circuits for Variational Inference in Discrete Graphical Models Andy Shih Stefano Ermon TPM 18 20 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 136 48 0 20 Oct 2020
Kohn-Sham equations as regularizer: building prior knowledge into machine-learned physics Li Li Stephan Hoyer Ryan Pederson Ruoxi Sun E. D. Cubuk Patrick F. Riley K. Burke AI4CE 32 120 0 17 Sep 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 32 7 0 05 Jun 2020
Path Sample-Analytic Gradient Estimators for Stochastic Binary Networks Alexander Shekhovtsov V. Yanush B. Flach MQ 34 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
Robust Training of Vector Quantized Bottleneck Models A. Lancucki J. Chorowski Guillaume Sanchez R. Marxer Nanxin Chen Hans J. G. A. Dolfing Sameer Khurana Tanel Alumäe Antoine Laurent 15 58 0 18 May 2020
Inverse Graphics GAN: Learning to Generate 3D Shapes from Unstructured 2D Data Sebastian Lunz Yingzhen Li Andrew Fitzgibbon Nate Kushman 3DV GAN 17 54 0 28 Feb 2020
Learning in the Frequency Domain Kai Xu Minghai Qin Fei Sun Yuhao Wang Yen-kuang Chen Fengbo Ren 39 393 0 27 Feb 2020
Estimating Gradients for Discrete Random Variables by Sampling without Replacement W. Kool H. V. Hoof Max Welling BDL 25 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
UNAS: Differentiable Architecture Search Meets Reinforcement Learning Arash Vahdat Arun Mallya Ming-Yu Liu Jan Kautz 30 33 0 16 Dec 2019
Learning with Multiplicative Perturbations Xiulong Yang Shihao Ji AAML 14 4 0 04 Dec 2019
ARSM Gradient Estimator for Supervised Learning to Rank Siamak Zamani Dadaneh Shahin Boluki Mingyuan Zhou Xiaoning Qian 17 7 0 01 Nov 2019
A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning Xuanqing Liu Si Si Xiaojin Zhu Yang Li Cho-Jui Hsieh AAML 27 76 0 30 Oct 2019
Collapsed Amortized Variational Inference for Switching Nonlinear Dynamical Systems Zhe Dong Bryan Seybold Kevin Patrick Murphy Hung Bui BDL 24 30 0 21 Oct 2019
Straight-Through Estimator as Projected Wasserstein Gradient Flow Pengyu Cheng YooJung Choi Yitao Liang Dinghan Shen Ricardo Henao Guy Van den Broeck 24 14 0 05 Oct 2019
Intensity-Free Learning of Temporal Point Processes Oleksandr Shchur Marin Bilos Stephan Günnemann AI4TS 19 167 0 26 Sep 2019
Monte Carlo Gradient Estimation in Machine Learning S. Mohamed Mihaela Rosca Michael Figurnov A. Mnih 26 397 0 25 Jun 2019
Near-Optimal Glimpse Sequences for Improved Hard Attention Neural Network Training William Harvey Michael Teng Frank D. Wood 25 4 0 13 Jun 2019
Improving Discrete Latent Representations With Differentiable Approximation Bridges Jason Ramapuram Russ Webb DRL 11 9 0 09 May 2019
Routing Networks and the Challenges of Modular and Compositional Computation Clemens Rosenbaum Ignacio Cases Matthew D Riemer Tim Klinger 34 78 0 29 Apr 2019
From Variational to Deterministic Autoencoders Partha Ghosh Mehdi S. M. Sajjadi Antonio Vergari Michael J. Black Bernhard Schölkopf DRL 17 269 0 29 Mar 2019
Cooperative Learning of Disjoint Syntax and Semantics Serhii Havrylov Germán Kruszewski Armand Joulin 18 48 0 25 Feb 2019
Neural Joint Source-Channel Coding Kristy Choi Kedar Tatwawadi Aditya Grover Tsachy Weissman Stefano Ermon 13 38 0 19 Nov 2018
Pathwise Derivatives Beyond the Reparameterization Trick M. Jankowiak F. Obermeyer 22 110 0 05 Jun 2018
Reparameterization Gradient for Non-differentiable Models Wonyeol Lee Hangyeol Yu Hongseok Yang DRL 17 30 0 01 Jun 2018