A-NICE-MC: Adversarial Training for MCMC

23 June 2017

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

34 / 34 papers shown

Title
Score-Based Metropolis-Hastings Algorithms Ahmed Aloui Ali Hasan Juncheng Dong Zihao Wu Vahid Tarokh DiffM 43 0 0 31 Dec 2024
Hitchhiker's guide on the relation of Energy-Based Models with other generative models, sampling and statistical physics: a comprehensive review Davide Carbone 38 1 0 19 Jun 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 49 2 0 02 Oct 2023
Probabilistic Regular Tree Priors for Scientific Symbolic Reasoning Tim Schneider A. Totounferoush Wolfgang Nowak Steffen Staab 26 0 0 14 Jun 2023
Optimal Preconditioning and Fisher Adaptive Langevin Sampling Michalis K. Titsias 35 11 0 23 May 2023
Machine Learning and the Future of Bayesian Computation Steven Winter Trevor Campbell Lizhen Lin Sanvesh Srivastava David B. Dunson TPM 47 4 0 21 Apr 2023
A Diffusion-based Method for Multi-turn Compositional Image Generation Chao Wang DiffM 38 3 0 05 Apr 2023
Versatile Diffusion: Text, Images and Variations All in One Diffusion Model Xingqian Xu Zhangyang Wang Eric Zhang Kai Wang Humphrey Shi DiffM 43 186 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 32 33 0 14 Nov 2022
Path Integral Sampler: a stochastic control approach for sampling Qinsheng Zhang Yongxin Chen DiffM 18 101 0 30 Nov 2021
Estimating High Order Gradients of the Data Distribution by Denoising Chenlin Meng Yang Song Wenzhe Li Stefano Ermon DiffM 13 45 0 08 Nov 2021
Semi-Empirical Objective Functions for MCMC Proposal Optimization Chris Cannella Vahid Tarokh 31 1 0 03 Jun 2021
Unbiased Monte Carlo Cluster Updates with Autoregressive Neural Networks Dian Wu R. Rossi Giuseppe Carleo 26 29 0 12 May 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 41 483 0 08 Mar 2021
Orbital MCMC Kirill Neklyudov Max Welling 26 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 21 22 0 15 Oct 2020
A Neural Network MCMC sampler that maximizes Proposal Entropy Zengyi Li Yubei Chen Friedrich T. Sommer 27 14 0 07 Oct 2020
Denoising Diffusion Implicit Models Jiaming Song Chenlin Meng Stefano Ermon VLM DiffM 56 6,972 0 06 Oct 2020
Involutive MCMC: a Unifying Framework Kirill Neklyudov Max Welling Evgenii Egorov Dmitry Vetrov 18 36 0 30 Jun 2020
Denoising Diffusion Probabilistic Models Jonathan Ho Ajay Jain Pieter Abbeel DiffM 118 17,042 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 23 93 0 16 Jun 2020
Batch Stationary Distribution Estimation Junfeng Wen Bo Dai Lihong Li Dale Schuurmans OffRL 22 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 31 87 0 17 Feb 2020
Stochastic Normalizing Flows Hao Wu Jonas Köhler Frank Noé 57 176 0 16 Feb 2020
i-flow: High-dimensional Integration and Sampling with Normalizing Flows Christina Gao J. Isaacson Claudius Krause AI4CE 16 107 0 15 Jan 2020
Normalizing Flows for Probabilistic Modeling and Inference George Papamakarios Eric T. Nalisnick Danilo Jimenez Rezende S. Mohamed Balaji Lakshminarayanan TPM AI4CE 57 1,631 0 05 Dec 2019
Gradient-based Adaptive Markov Chain Monte Carlo Michalis K. Titsias P. Dellaportas BDL 39 22 0 04 Nov 2019
Metropolis-Hastings view on variational inference and adversarial training Kirill Neklyudov Evgenii Egorov Pavel Shvechikov Dmitry Vetrov GAN 29 13 0 16 Oct 2018
Stein Neural Sampler Tianyang Hu Zixiang Chen Hanxi Sun Jincheng Bai Mao Ye Guang Cheng SyDa GAN 22 34 0 08 Oct 2018
Meta-Learning for Stochastic Gradient MCMC Wenbo Gong Yingzhen Li José Miguel Hernández-Lobato BDL 36 44 0 12 Jun 2018
Neural Autoregressive Flows Chin-Wei Huang David M. Krueger Alexandre Lacoste Aaron Courville DRL AI4CE 28 433 0 03 Apr 2018
Neural Network Renormalization Group Shuo-Hui Li Lei Wang BDL DRL 31 126 0 08 Feb 2018
Measuring Sample Quality with Kernels Jackson Gorham Lester W. Mackey 86 223 0 06 Mar 2017
MCMC using Hamiltonian dynamics Radford M. Neal 185 3,267 0 09 Jun 2012