Variational inference for Monte Carlo objectives

22 February 2016

A. Mnih

Danilo Jimenez Rezende

DRL

BDL

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Papers citing "Variational inference for Monte Carlo objectives"

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Title
CARMS: Categorical-Antithetic-REINFORCE Multi-Sample Gradient Estimator Alek Dimitriev Mingyuan Zhou 6 7 0 26 Oct 2021
Guiding Visual Question Generation Nihir Vedd Zixu Wang Marek Rei Yishu Miao Lucia Specia 81 23 0 15 Oct 2021
Hindsight Network Credit Assignment: Efficient Credit Assignment in Networks of Discrete Stochastic Units K. Young 23 0 0 14 Oct 2021
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
Hybrid Memoised Wake-Sleep: Approximate Inference at the Discrete-Continuous Interface T. Le K. M. Collins Luke B. Hewitt Kevin Ellis N. Siddharth S. Gershman J. Tenenbaum 122 5 0 04 Jul 2021
Monte Carlo Variational Auto-Encoders Achille Thin Nikita Kotelevskii Arnaud Doucet Alain Durmus Eric Moulines Maxim Panov BDL DRL 19 44 0 30 Jun 2021
Learning to Time-Decode in Spiking Neural Networks Through the Information Bottleneck N. Skatchkovsky Osvaldo Simeone Hyeryung Jang 25 20 0 02 Jun 2021
ARMS: Antithetic-REINFORCE-Multi-Sample Gradient for Binary Variables Alek Dimitriev Mingyuan Zhou 14 12 0 28 May 2021
Monte Carlo Filtering Objectives: A New Family of Variational Objectives to Learn Generative Model and Neural Adaptive Proposal for Time Series Shuangshuang Chen Sihao Ding Y. Karayiannidis Mårten Björkman BDL AI4TS 20 2 0 20 May 2021
Probabilistic Mixture-of-Experts for Efficient Deep Reinforcement Learning Jie Ren Yewen Li Zihan Ding Wei Pan Hao Dong BDL MoE 8 25 0 19 Apr 2021
Storchastic: A Framework for General Stochastic Automatic Differentiation Emile van Krieken Jakub M. Tomczak A. T. Teije ODL OffRL 23 15 0 01 Apr 2021
Annealed Flow Transport Monte Carlo Michael Arbel A. G. Matthews Arnaud Doucet 28 69 0 15 Feb 2021
Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting Longyuan Li Junchi Yan Xiaokang Yang Yaohui Jin OOD BDL AI4TS 37 60 0 31 Jan 2021
Mind the Gap when Conditioning Amortised Inference in Sequential Latent-Variable Models Justin Bayer Maximilian Soelch Atanas Mirchev Baris Kayalibay Patrick van der Smagt 19 15 0 18 Jan 2021
Planning from Pixels in Atari with Learned Symbolic Representations Andrea Dittadi Frederik K. Drachmann Thomas Bolander 16 11 0 16 Dec 2020
Mutual Information Constraints for Monte-Carlo Objectives Gábor Melis András Gyorgy Phil Blunsom 16 1 0 01 Dec 2020
Improved Variational Bayesian Phylogenetic Inference with Normalizing Flows Cheng Zhang BDL 18 27 0 01 Dec 2020
Latent Template Induction with Gumbel-CRFs Yao Fu Chuanqi Tan Bin Bi Mosha Chen Yansong Feng Alexander M. Rush BDL 11 12 0 29 Nov 2020
On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes Tim G. J. Rudner Oscar Key Y. Gal Tom Rainforth 6 3 0 01 Nov 2020
Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective Vu-Linh Nguyen Vaden Masrani Rob Brekelmans Michael A. Osborne Frank D. Wood 11 5 0 29 Oct 2020
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
PAC $^m$ -Bayes: Narrowing the Empirical Risk Gap in the Misspecified Bayesian Regime Warren Morningstar Alexander A. Alemi Joshua V. Dillon 79 16 0 19 Oct 2020
Rao-Blackwellizing the Straight-Through Gumbel-Softmax Gradient Estimator Max B. Paulus Chris J. Maddison Andreas Krause BDL 39 38 0 09 Oct 2020
Optimal Variance Control of the Score Function Gradient Estimator for Importance Weighted Bounds Valentin Liévin Andrea Dittadi Anders Christensen Ole Winther DRL 14 6 0 05 Aug 2020
Approximation Based Variance Reduction for Reparameterization Gradients Tomas Geffner Justin Domke BDL DRL 19 10 0 29 Jul 2020
Multi-Sample Online Learning for Probabilistic Spiking Neural Networks Hyeryung Jang Osvaldo Simeone 11 0 0 23 Jul 2020
Learning to learn generative programs with Memoised Wake-Sleep Luke B. Hewitt T. Le J. Tenenbaum CLL 12 26 0 06 Jul 2020
Efficient Marginalization of Discrete and Structured Latent Variables via Sparsity Gonçalo M. Correia Vlad Niculae Wilker Aziz André F. T. Martins BDL 22 22 0 03 Jul 2020
Semi-supervised Sequential Generative Models Michael Teng T. Le Adam Scibior Frank D. Wood BDL AI4TS 25 3 0 30 Jun 2020
Lattice Representation Learning Luis A. Lastras 9 1 0 24 Jun 2020
DisARM: An Antithetic Gradient Estimator for Binary Latent Variables Zhe Dong A. Mnih George Tucker DRL 6 32 0 18 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
Posterior Control of Blackbox Generation Xiang Lisa Li Alexander M. Rush 19 25 0 10 May 2020
VOWEL: A Local Online Learning Rule for Recurrent Networks of Probabilistic Spiking Winner-Take-All Circuits Hyeryung Jang N. Skatchkovsky Osvaldo Simeone 22 11 0 20 Apr 2020
Learning Discrete Structured Representations by Adversarially Maximizing Mutual Information K. Stratos Sam Wiseman SSL DRL 6 7 0 08 Apr 2020
Infomax Neural Joint Source-Channel Coding via Adversarial Bit Flip Yuxuan Song Minkai Xu Lantao Yu Hao Zhou Shuo Shao Yong Yu AAML 19 16 0 03 Apr 2020
Neural Communication Systems with Bandwidth-limited Channel Karen Ullrich Fabio Viola Danilo Jimenez Rezende 15 1 0 30 Mar 2020
A Better Variant of Self-Critical Sequence Training Ruotian Luo BDL 22 37 0 22 Mar 2020
Generalized Gumbel-Softmax Gradient Estimator for Generic Discrete Random Variables Weonyoung Joo Dongjun Kim Seung-Jae Shin Il-Chul Moon 21 1 0 04 Mar 2020
VQ-DRAW: A Sequential Discrete VAE Alex Nichol BDL 12 3 0 03 Mar 2020
Analysis of diversity-accuracy tradeoff in image captioning Ruotian Luo Gregory Shakhnarovich 9 12 0 27 Feb 2020
Estimating Gradients for Discrete Random Variables by Sampling without Replacement W. Kool H. V. Hoof Max Welling BDL 23 49 0 14 Feb 2020
Joint Distributions for TensorFlow Probability Dan Piponi Dave Moore Joshua V. Dillon GP 19 16 0 22 Jan 2020
Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax Andres Potapczynski G. Loaiza-Ganem John P. Cunningham 29 29 0 19 Dec 2019
A Rule for Gradient Estimator Selection, with an Application to Variational Inference Tomas Geffner Justin Domke 37 6 0 05 Nov 2019
Amortized Population Gibbs Samplers with Neural Sufficient Statistics Hao Wu Heiko Zimmermann Eli Sennesh T. Le Jan Willem van de Meent 17 7 0 04 Nov 2019
Energy-Inspired Models: Learning with Sampler-Induced Distributions Dieterich Lawson George Tucker Bo Dai Rajesh Ranganath 16 31 0 31 Oct 2019
Prior Specification for Bayesian Matrix Factorization via Prior Predictive Matching Eliezer de Souza da Silva Tomasz Ku'smierczyk M. Hartmann Arto Klami 14 2 0 27 Oct 2019
Customizing Sequence Generation with Multi-Task Dynamical Systems Alex Bird Christopher K. I. Williams AI4CE 20 11 0 11 Oct 2019