Towards reduction of autocorrelation in HMC by machine learning

11 December 2017

Papers citing "Towards reduction of autocorrelation in HMC by machine learning"

10 / 10 papers shown

Title
Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics Kyle Cranmer G. Kanwar S. Racanière Danilo Jimenez Rezende P. Shanahan AI4CE 37 27 0 03 Sep 2023
Anomaly Detection in Aeronautics Data with Quantum-compatible Discrete Deep Generative Model T. Templin Milad Memarzadeh W. Vinci P. A. Lott A. A. Asanjan Anthony Alexiades Armenakas E. Rieffel DRL 24 5 0 22 Mar 2023
Toward Unlimited Self-Learning MCMC with Parallel Adaptive Annealing Yuma Ichikawa Akira Nakagawa Hiromoto Masayuki Yuhei Umeda BDL 20 0 0 25 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 37 33 0 14 Nov 2022
Learning Lattice Quantum Field Theories with Equivariant Continuous Flows Mathis Gerdes P. D. Haan Corrado Rainone Roberto Bondesan Miranda C. N. Cheng AI4CE 24 40 0 01 Jul 2022
Applications of Machine Learning to Lattice Quantum Field Theory D. Boyda Salvatore Cali Sam Foreman L. Funcke D. Hackett ... Gert Aarts A. Alexandru Xiao-Yong Jin B. Lucini P. Shanahan AI4CE 32 19 0 10 Feb 2022
Machine Learning of Thermodynamic Observables in the Presence of Mode Collapse K. Nicoli Christopher J. Anders L. Funcke T. Hartung K. Jansen Pan Kessel Shinichi Nakajima Paolo Stornati AI4CE 42 13 0 22 Nov 2021
Machine learning spectral functions in lattice QCD S.-Y. Chen H. Ding Feng Liu G. Papp C.-B. Yang 24 20 0 26 Oct 2021
Deep Learning Hamiltonian Monte Carlo Sam Foreman Xiao-Yong Jin James C. Osborn 22 16 0 07 May 2021
A high-bias, low-variance introduction to Machine Learning for physicists Pankaj Mehta Marin Bukov Ching-Hao Wang A. G. Day C. Richardson Charles K. Fisher D. Schwab AI4CE 28 867 0 23 Mar 2018