Generalizing Hamiltonian Monte Carlo with Neural Networks

25 November 2017

Daniel Levy

Matthew D. Hoffman

Jascha Narain Sohl-Dickstein

BDL

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Papers citing "Generalizing Hamiltonian Monte Carlo with Neural Networks"

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Title
Training neural control variates using correlated configurations Hyunwoo Oh BDL 35 0 0 12 May 2025
Training Neural Samplers with Reverse Diffusive KL Divergence Jiajun He Wenlin Chen Mingtian Zhang David Barber José Miguel Hernández-Lobato DiffM 37 4 0 16 Oct 2024
S $^2$ AC: Energy-Based Reinforcement Learning with Stein Soft Actor Critic Safa Messaoud Billel Mokeddem Zhenghai Xue L. Pang Bo An Haipeng Chen Sanjay Chawla 41 3 0 02 May 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 45 2 0 02 Oct 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 45 4 0 21 Apr 2023
A Diffusion-based Method for Multi-turn Compositional Image Generation Chao Wang DiffM 33 3 0 05 Apr 2023
Toward Unlimited Self-Learning MCMC with Parallel Adaptive Annealing Yuma Ichikawa Akira Nakagawa Hiromoto Masayuki Yuhei Umeda BDL 18 0 0 25 Nov 2022
VeLO: Training Versatile Learned Optimizers by Scaling Up Luke Metz James Harrison C. Freeman Amil Merchant Lucas Beyer ... Naman Agrawal Ben Poole Igor Mordatch Adam Roberts Jascha Narain Sohl-Dickstein 29 60 0 17 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
Denoising MCMC for Accelerating Diffusion-Based Generative Models Beomsu Kim Jong Chul Ye DiffM 49 13 0 29 Sep 2022
Flow Annealed Importance Sampling Bootstrap Laurence Illing Midgley Vincent Stimper G. Simm Bernhard Schölkopf José Miguel Hernández-Lobato 32 77 0 03 Aug 2022
Enhanced gradient-based MCMC in discrete spaces Benjamin Rhodes Michael U. Gutmann 29 15 0 29 Jul 2022
Accelerating Hamiltonian Monte Carlo via Chebyshev Integration Time Jun-Kun Wang Andre Wibisono 27 9 0 05 Jul 2022
Parallel Tempering With a Variational Reference Nikola Surjanovic Saifuddin Syed Alexandre Bouchard-Coté Trevor Campbell 28 11 0 31 May 2022
Practical tradeoffs between memory, compute, and performance in learned optimizers Luke Metz C. Freeman James Harrison Niru Maheswaranathan Jascha Narain Sohl-Dickstein 33 32 0 22 Mar 2022
Machine Learning in Nuclear Physics A. Boehnlein M. Diefenthaler C. Fanelli M. Hjorth-Jensen T. Horn ... M. Schram A. Scheinker Michael S. Smith Xin-Nian Wang Veronique Ziegler AI4CE 37 41 0 04 Dec 2021
Hamiltonian Dynamics with Non-Newtonian Momentum for Rapid Sampling Greg Ver Steeg Aram Galstyan 41 13 0 03 Nov 2021
Delayed rejection Hamiltonian Monte Carlo for sampling multiscale distributions Chirag Modi A. Barnett Bob Carpenter 36 14 0 01 Oct 2021
Solution of Physics-based Bayesian Inverse Problems with Deep Generative Priors Dhruv V. Patel Deep Ray Assad A. Oberai AI4CE 13 37 0 06 Jul 2021
Semi-Empirical Objective Functions for MCMC Proposal Optimization Chris Cannella Vahid Tarokh 28 1 0 03 Jun 2021
NEO: Non Equilibrium Sampling on the Orbit of a Deterministic Transform Achille Thin Yazid Janati Sylvain Le Corff Charles Ollion Arnaud Doucet Alain Durmus Eric Moulines C. Robert 25 7 0 17 Mar 2021
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 6,944 0 06 Oct 2020
Involutive MCMC: a Unifying Framework Kirill Neklyudov Max Welling Evgenii Egorov Dmitry Vetrov 16 36 0 30 Jun 2020
Denoising Diffusion Probabilistic Models Jonathan Ho Ajay Jain Pieter Abbeel DiffM 118 16,947 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 92 0 16 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
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
Gradient-based Adaptive Markov Chain Monte Carlo Michalis K. Titsias P. Dellaportas BDL 33 22 0 04 Nov 2019
Hamiltonian Generative Networks Peter Toth Danilo Jimenez Rezende Andrew Jaegle S. Racanière Aleksandar Botev I. Higgins BDL DRL AI4CE GAN 21 215 0 30 Sep 2019
Transport Monte Carlo: High-Accuracy Posterior Approximation via Random Transport L. Duan OT 29 11 0 24 Jul 2019
$$(1 + \varepsilon)$-class Classification: an Anomaly Detection Method for Highly Imbalanced or Incomplete Data Sets$ $(1 + \varepsilon)$ -class Classification: an Anomaly Detection Method for Highly Imbalanced or Incomplete Data Sets M. Borisyak Artem Sergeevich Ryzhikov Andrey Ustyuzhanin D. Derkach Fedor Ratnikov Olga Mineeva 7 4 0 14 Jun 2019
Learning Symmetries of Classical Integrable Systems Roberto Bondesan A. Lamacraft 22 39 0 11 Jun 2019
NeuTra-lizing Bad Geometry in Hamiltonian Monte Carlo Using Neural Transport Matthew Hoffman Pavel Sountsov Joshua V. Dillon I. Langmore Dustin Tran Srinivas Vasudevan BDL 27 103 0 09 Mar 2019
Metropolis-Hastings view on variational inference and adversarial training Kirill Neklyudov Evgenii Egorov Pavel Shvechikov Dmitry Vetrov GAN 26 13 0 16 Oct 2018
Meta-Learning for Stochastic Gradient MCMC Wenbo Gong Yingzhen Li José Miguel Hernández-Lobato BDL 24 44 0 12 Jun 2018
Deep Energy Estimator Networks Saeed Saremi Arash Mehrjou Bernhard Schölkopf Aapo Hyvarinen 14 73 0 21 May 2018
Conditional Inference in Pre-trained Variational Autoencoders via Cross-coding Ga Wu Justin Domke Scott Sanner BDL DRL 21 11 0 20 May 2018
Modified Hamiltonian Monte Carlo for Bayesian inference Tijana Radivojević E. Akhmatskaya 14 31 0 13 Jun 2017