A-NICE-MC: Adversarial Training for MCMC

23 June 2017

Papers citing "A-NICE-MC: Adversarial Training for MCMC"

26 / 26 papers shown

Title
Score-Based Metropolis-Hastings Algorithms Ahmed Aloui Ali Hasan Juncheng Dong Zihao Wu Vahid Tarokh DiffM 34 0 0 31 Dec 2024
Energy-Guided Continuous Entropic Barycenter Estimation for General Costs Alexander Kolesov Petr Mokrov Igor Udovichenko Milena Gazdieva G. Pammer Anastasis Kratsios Evgeny Burnaev Alexander Korotin OT 34 2 0 02 Oct 2023
Probabilistic Regular Tree Priors for Scientific Symbolic Reasoning Tim Schneider A. Totounferoush Wolfgang Nowak Steffen Staab 16 0 0 14 Jun 2023
Machine Learning and the Future of Bayesian Computation Steven Winter Trevor Campbell Lizhen Lin Sanvesh Srivastava David B. Dunson TPM 31 4 0 21 Apr 2023
Versatile Diffusion: Text, Images and Variations All in One Diffusion Model Xingqian Xu Zhangyang Wang Eric Zhang Kai Wang Humphrey Shi DiffM 28 181 0 15 Nov 2022
Aspects of scaling and scalability for flow-based sampling of lattice QCD Ryan Abbott M. S. Albergo Aleksandar Botev D. Boyda Kyle Cranmer ... Ali Razavi Danilo Jimenez Rezende F. Romero-López P. Shanahan Julian M. Urban 26 33 0 14 Nov 2022
Path Integral Sampler: a stochastic control approach for sampling Qinsheng Zhang Yongxin Chen DiffM 13 101 0 30 Nov 2021
Estimating High Order Gradients of the Data Distribution by Denoising Chenlin Meng Yang Song Wenzhe Li Stefano Ermon DiffM 8 44 0 08 Nov 2021
Semi-Empirical Objective Functions for MCMC Proposal Optimization Chris Cannella Vahid Tarokh 20 1 0 03 Jun 2021
Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models Sam Bond-Taylor Adam Leach Yang Long Chris G. Willcocks VLM TPM 36 478 0 08 Mar 2021
Orbital MCMC Kirill Neklyudov Max Welling 13 7 0 15 Oct 2020
A Hamiltonian Monte Carlo Method for Probabilistic Adversarial Attack and Learning Hongjun Wang Guanbin Li Xiaobai Liu Liang Lin GAN AAML 11 22 0 15 Oct 2020
A Neural Network MCMC sampler that maximizes Proposal Entropy Zengyi Li Yubei Chen Friedrich T. Sommer 25 14 0 07 Oct 2020
Denoising Diffusion Implicit Models Jiaming Song Chenlin Meng Stefano Ermon VLM DiffM 56 6,932 0 06 Oct 2020
Involutive MCMC: a Unifying Framework Kirill Neklyudov Max Welling Evgenii Egorov Dmitry Vetrov 8 36 0 30 Jun 2020
Denoising Diffusion Probabilistic Models Jonathan Ho Ajay Jain Pieter Abbeel DiffM 116 16,901 0 19 Jun 2020
Understanding and Mitigating Exploding Inverses in Invertible Neural Networks Jens Behrmann Paul Vicol Kuan-Chieh Jackson Wang Roger C. Grosse J. Jacobsen 8 92 0 16 Jun 2020
Batch Stationary Distribution Estimation Junfeng Wen Bo Dai Lihong Li Dale Schuurmans OffRL 9 22 0 02 Mar 2020
Augmented Normalizing Flows: Bridging the Gap Between Generative Flows and Latent Variable Models Chin-Wei Huang Laurent Dinh Aaron Courville DRL 24 87 0 17 Feb 2020
Stochastic Normalizing Flows Hao Wu Jonas Köhler Frank Noé 37 176 0 16 Feb 2020
Gradient-based Adaptive Markov Chain Monte Carlo Michalis K. Titsias P. Dellaportas BDL 21 22 0 04 Nov 2019
Metropolis-Hastings view on variational inference and adversarial training Kirill Neklyudov Evgenii Egorov Pavel Shvechikov Dmitry Vetrov GAN 17 13 0 16 Oct 2018
Meta-Learning for Stochastic Gradient MCMC Wenbo Gong Yingzhen Li José Miguel Hernández-Lobato BDL 16 44 0 12 Jun 2018
Neural Autoregressive Flows Chin-Wei Huang David M. Krueger Alexandre Lacoste Aaron Courville DRL AI4CE 14 432 0 03 Apr 2018
Measuring Sample Quality with Kernels Jackson Gorham Lester W. Mackey 86 222 0 06 Mar 2017
MCMC using Hamiltonian dynamics Radford M. Neal 185 3,262 0 09 Jun 2012