Metropolis-Hastings view on variational inference and adversarial
training

Metropolis-Hastings view on variational inference and adversarial training

16 October 2018

Kirill Neklyudov

Pavel Shvechikov

Dmitry Vetrov

Papers citing "Metropolis-Hastings view on variational inference and adversarial training"

7 / 7 papers shown

Title
AutoStep: Locally adaptive involutive MCMC Tiange Liu Nikola Surjanovic Miguel Biron-Lattes Alexandre Bouchard-Coté Trevor Campbell 28 1 0 24 Oct 2024
Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients Kyurae Kim Jisu Oh J. Gardner Adji Bousso Dieng Hongseok Kim BDL 24 8 0 13 Jun 2022
Semi-Empirical Objective Functions for MCMC Proposal Optimization Chris Cannella Vahid Tarokh 25 1 0 03 Jun 2021
A Neural Network MCMC sampler that maximizes Proposal Entropy Zengyi Li Yubei Chen Friedrich T. Sommer 25 14 0 07 Oct 2020
Gradient-based Adaptive Markov Chain Monte Carlo Michalis K. Titsias P. Dellaportas BDL 25 22 0 04 Nov 2019
Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference Y. Gal Zoubin Ghahramani UQCV BDL 197 745 0 06 Jun 2015
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 282 9,136 0 06 Jun 2015