Flow-based generative models for Markov chain Monte Carlo in lattice field theory

26 April 2019

Papers citing "Flow-based generative models for Markov chain Monte Carlo in lattice field theory"

49 / 49 papers shown

Title
Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching Aaron J. Havens Benjamin Kurt Miller Bing Yan Carles Domingo-Enrich Anuroop Sriram ... Brandon Amos Brian Karrer Xiang Fu Guan-Horng Liu Ricky T. Q. Chen DiffM 55 0 0 16 Apr 2025
Multilevel Generative Samplers for Investigating Critical Phenomena Ankur Singha E. Cellini K. Nicoli K. Jansen Stefan Kühn Shinichi Nakajima 64 1 0 11 Mar 2025
Single-Step Consistent Diffusion Samplers Pascal Jutras-Dubé Patrick Pynadath Ruqi Zhang DiffM 80 0 0 17 Feb 2025
From discrete-time policies to continuous-time diffusion samplers: Asymptotic equivalences and faster training Julius Berner Lorenz Richter Marcin Sendera Jarrid Rector-Brooks Nikolay Malkin OffRL 65 3 0 10 Jan 2025
On learning higher-order cumulants in diffusion models Gert Aarts Diaa E. Habibi Lei Wang K. Zhou 28 4 0 28 Oct 2024
NETS: A Non-Equilibrium Transport Sampler M. S. Albergo Eric Vanden-Eijnden DiffM 57 10 0 03 Oct 2024
Neural Thermodynamic Integration: Free Energies from Energy-based Diffusion Models Bálint Máté François Fleuret Tristan Bereau DiffM 46 2 0 04 Jun 2024
Fast and Unified Path Gradient Estimators for Normalizing Flows Lorenz Vaitl Ludwig Winkler Lorenz Richter Pan Kessel 44 4 0 23 Mar 2024
Iterated Denoising Energy Matching for Sampling from Boltzmann Densities Tara Akhound-Sadegh Jarrid Rector-Brooks A. Bose Sarthak Mittal Pablo Lemos ... Siamak Ravanbakhsh Gauthier Gidel Yoshua Bengio Nikolay Malkin Alexander Tong DiffM 45 42 0 09 Feb 2024
Improved off-policy training of diffusion samplers Marcin Sendera Minsu Kim Sarthak Mittal Pablo Lemos Luca Scimeca Jarrid Rector-Brooks Alexandre Adam Yoshua Bengio Nikolay Malkin OffRL 71 18 0 07 Feb 2024
Combining Normalizing Flows and Quasi-Monte Carlo Charly Andral BDL 37 1 0 11 Jan 2024
Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization Dinghuai Zhang Ricky Tian Qi Chen Cheng-Hao Liu Aaron C. Courville Yoshua Bengio 39 41 0 04 Oct 2023
Balanced Training of Energy-Based Models with Adaptive Flow Sampling Louis Grenioux Eric Moulines Marylou Gabrié 26 2 0 01 Jun 2023
Mutual information of spin systems from autoregressive neural networks P. Białas P. Korcyl T. Stebel 32 3 0 26 Apr 2023
Neural Diffeomorphic Non-uniform B-spline Flows S. Hong S. Chun 40 1 0 07 Apr 2023
Locality-constrained autoregressive cum conditional normalizing flow for lattice field theory simulations R. DineshP. AI4CE 22 0 0 04 Apr 2023
Fluctuation without dissipation: Microcanonical Langevin Monte Carlo Jakob Robnik U. Seljak 49 6 0 31 Mar 2023
Rigid Body Flows for Sampling Molecular Crystal Structures Jonas Köhler Michele Invernizzi P. D. Haan Frank Noé AI4CE 44 27 0 26 Jan 2023
fintech-kMC: Agent based simulations of financial platforms for design and testing of machine learning systems Isaac Tamblyn Tengkai Yu Ian Benlolo 13 0 0 04 Jan 2023
Simulating 2+1D Lattice Quantum Electrodynamics at Finite Density with Neural Flow Wavefunctions Zhuo Chen Di Luo Kaiwen Hu B. Clark 27 14 0 14 Dec 2022
Toward Unlimited Self-Learning MCMC with Parallel Adaptive Annealing Yuma Ichikawa Akira Nakagawa Hiromoto Masayuki Yuhei Umeda BDL 18 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 32 33 0 14 Nov 2022
Blind Super-Resolution for Remote Sensing Images via Conditional Stochastic Normalizing Flows Hanlin Wu Ning Ni Shan Wang Li-bao Zhang 38 8 0 14 Oct 2022
Deep Variational Free Energy Approach to Dense Hydrogen H.-j. Xie Ziqun Li Han Wang Linfeng Zhang Lei Wang 49 9 0 13 Sep 2022
Algorithmic Differentiation for Automated Modeling of Machine Learned Force Fields Niklas Schmitz Klaus-Robert Muller Stefan Chmiela AI4CE 29 11 0 25 Aug 2022
Gradients should stay on Path: Better Estimators of the Reverse- and Forward KL Divergence for Normalizing Flows Lorenz Vaitl K. Nicoli Shinichi Nakajima Pan Kessel 61 24 0 17 Jul 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
Deterministic Langevin Monte Carlo with Normalizing Flows for Bayesian Inference R. Grumitt B. Dai U. Seljak BDL 29 13 0 27 May 2022
Hierarchical autoregressive neural networks for statistical systems P. Białas P. Korcyl T. Stebel 24 11 0 21 Mar 2022
Symmetry Group Equivariant Architectures for Physics A. Bogatskiy S. Ganguly Thomas Kipf Risi Kondor David W. Miller ... Jan T. Offermann M. Pettee P. Shanahan C. Shimmin S. Thais AI4CE 27 27 0 11 Mar 2022
A Group-Equivariant Autoencoder for Identifying Spontaneously Broken Symmetries Devanshu Agrawal A. Del Maestro Steven Johnston James Ostrowski DRL AI4CE 36 2 0 13 Feb 2022
Gradient estimators for normalising flows P. Białas P. Korcyl T. Stebel BDL 27 3 0 02 Feb 2022
Continual Repeated Annealed Flow Transport Monte Carlo A. G. Matthews Michael Arbel Danilo Jimenez Rezende Arnaud Doucet OT 37 46 0 31 Jan 2022
Predicting Physics in Mesh-reduced Space with Temporal Attention Xu Han Han Gao Tobias Pfaff Jian-Xun Wang Liping Liu AI4CE 21 73 0 22 Jan 2022
Stochastic normalizing flows as non-equilibrium transformations M. Caselle E. Cellini A. Nada M. Panero 36 34 0 21 Jan 2022
Machine Learning Trivializing Maps: A First Step Towards Understanding How Flow-Based Samplers Scale Up L. Debbio Joe Marsh Rossney Michael Wilson 16 6 0 31 Dec 2021
Machine Learning in the Search for New Fundamental Physics G. Karagiorgi Gregor Kasieczka S. Kravitz Benjamin Nachman David Shih AI4CE 49 113 0 07 Dec 2021
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 42 41 0 04 Dec 2021
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 28 13 0 22 Nov 2021
Scaling Up Machine Learning For Quantum Field Theory with Equivariant Continuous Flows P. D. Haan Corrado Rainone Miranda C. N. Cheng Roberto Bondesan AI4CE 19 35 0 06 Oct 2021
Latent Space Refinement for Deep Generative Models R. Winterhalder Marco Bellagente Benjamin Nachman BDL GAN DRL DiffM 10 27 0 01 Jun 2021
Unbiased Monte Carlo Cluster Updates with Autoregressive Neural Networks Dian Wu R. Rossi Giuseppe Carleo 32 29 0 12 May 2021
Sampling in Combinatorial Spaces with SurVAE Flow Augmented MCMC P. Jaini Didrik Nielsen Max Welling BDL 43 10 0 04 Feb 2021
A Living Review of Machine Learning for Particle Physics Matthew Feickert Benjamin Nachman KELM AI4CE 31 178 0 02 Feb 2021
Sampling using $SU(N)$ gauge equivariant flows D. Boyda G. Kanwar S. Racanière Danilo Jimenez Rezende M. S. Albergo Kyle Cranmer D. Hackett P. Shanahan 31 127 0 12 Aug 2020
Extending machine learning classification capabilities with histogram reweighting Dimitrios Bachtis Gert Aarts B. Lucini 19 21 0 29 Apr 2020
Stochastic Normalizing Flows Hao Wu Jonas Köhler Frank Noé 57 176 0 16 Feb 2020
Targeted free energy estimation via learned mappings Peter Wirnsberger A. J. Ballard George Papamakarios Stuart Abercrombie S. Racanière Alexander Pritzel Danilo Jimenez Rezende Charles Blundell 27 86 0 12 Feb 2020
A Probability Density Theory for Spin-Glass Systems Gavin Hartnett Masoud Mohseni 21 4 0 03 Jan 2020