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Introduction to Normalizing Flows for Lattice Field Theory

20 January 2021

Danilo Jimenez Rezende

P. Shanahan

AI4CE

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Papers citing "Introduction to Normalizing Flows for Lattice Field Theory"

32 / 32 papers shown

Title
Group-Equivariant Diffusion Models for Lattice Field Theory Octavio Vega J. Komijani Aida El-Khadra M. Marinković DiffM 232 0 0 30 Oct 2025
Neural Triangular Transport Maps: A New Approach Towards Sampling in Lattice QCD Andrey Bryutkin Youssef Marzouk 171 0 0 15 Oct 2025
NeuMC -- a package for neural sampling for lattice field theoriesSoftwareX (SoftwareX), 2025 Piotr Bialas P. Korcyl T. Stebel Dawid Zapolski 208 1 0 14 Mar 2025
Fast and Unified Path Gradient Estimators for Normalizing FlowsInternational Conference on Learning Representations (ICLR), 2024 Lorenz Vaitl Ludwig Winkler Lorenz Richter Pan Kessel 224 5 0 23 Mar 2024
Multi-Lattice Sampling of Quantum Field Theories via Neural Operator-based Flows Bálint Máté Franccois Fleuret AI4CE 222 1 0 01 Jan 2024
Sampling Multimodal Distributions with the Vanilla Score: Benefits of Data-Based InitializationInternational Conference on Learning Representations (ICLR), 2023 Frederic Koehler T. Vuong DiffM SyDa 164 10 0 03 Oct 2023
Diffusion Models as Stochastic Quantization in Lattice Field TheoryJournal of High Energy Physics (JHEP), 2023 Lei Wang Gert Aarts Kai Zhou DiffM 198 23 0 29 Sep 2023
Accelerating Markov Chain Monte Carlo sampling with diffusion modelsComputer Physics Communications (CPC), 2023 N. Hunt-Smith W. Melnitchouk F. Ringer Nobuo Sato A. Thomas M. J. White DiffM 159 28 0 04 Sep 2023
Training normalizing flows with computationally intensive target probability distributionsComputer Physics Communications (CPC), 2023 P. Białas P. Korcyl T. Stebel 180 6 0 25 Aug 2023
Sampling the lattice Nambu-Goto string using Continuous Normalizing Flows M. Caselle E. Cellini A. Nada 194 18 0 03 Jul 2023
Mutual information of spin systems from autoregressive neural networksPhysical Review E (PRE), 2023 P. Białas P. Korcyl T. Stebel 134 4 0 26 Apr 2023
Fluctuation without dissipation: Microcanonical Langevin Monte CarloSymposium on Advances in Approximate Bayesian Inference (AABI), 2023 Jakob Robnik U. Seljak 269 10 0 31 Mar 2023
Geometrical aspects of lattice gauge equivariant convolutional neural networks J. Aronsson David I. Müller Daniel Schuh 155 10 0 20 Mar 2023
SE(3) diffusion model with application to protein backbone generationInternational Conference on Machine Learning (ICML), 2023 Jason Yim Brian L. Trippe Valentin De Bortoli Emile Mathieu Arnaud Doucet Regina Barzilay Tommi Jaakkola DiffM 361 277 0 05 Feb 2023
Learning Interpolations between Boltzmann Densities Bálint Máté Franccois Fleuret 389 37 0 18 Jan 2023
Simulating first-order phase transition with hierarchical autoregressive networksPhysical Review E (Phys. Rev. E), 2022 P. Białas Paul A. Czarnota P. Korcyl T. Stebel 135 4 0 09 Dec 2022
Aspects of scaling and scalability for flow-based sampling of lattice QCDEuropean Physical Journal A (EPJ A), 2022 Ryan Abbott M. S. Albergo Aleksandar Botev D. Boyda Kyle Cranmer ... Ali Razavi Danilo Jimenez Rezende F. Romero-López P. Shanahan Julian M. Urban 208 40 0 14 Nov 2022
Deformations of Boltzmann Distributions Bálint Máté Franccois Fleuret OT 161 2 0 25 Oct 2022
Learning Lattice Quantum Field Theories with Equivariant Continuous FlowsSciPost Physics (SciPost Phys.), 2022 Mathis Gerdes P. D. Haan Corrado Rainone Roberto Bondesan Miranda C. N. Cheng AI4CE 304 51 0 01 Jul 2022
Path-Gradient Estimators for Continuous Normalizing FlowsInternational Conference on Machine Learning (ICML), 2022 Lorenz Vaitl K. Nicoli Shinichi Nakajima Pan Kessel 146 14 0 17 Jun 2022
Gradient estimators for normalising flows P. Białas P. Korcyl T. Stebel BDL 107 3 0 02 Feb 2022
Continual Repeated Annealed Flow Transport Monte CarloInternational Conference on Machine Learning (ICML), 2022 A. G. Matthews Michael Arbel Danilo Jimenez Rezende Arnaud Doucet OT 336 65 0 31 Jan 2022
Stochastic normalizing flows as non-equilibrium transformationsJournal of High Energy Physics (JHEP), 2022 M. Caselle E. Cellini A. Nada M. Panero 207 38 0 21 Jan 2022
LeapfrogLayers: A Trainable Framework for Effective Topological Sampling Sam Foreman Xiao-Yong Jin James C. Osborn 137 9 0 02 Dec 2021
Analysis of autocorrelation times in Neural Markov Chain Monte Carlo simulationsPhysical Review E (PRE), 2021 P. Białas P. Korcyl T. Stebel 134 12 0 19 Nov 2021
Lattice gauge symmetry in neural networks Matteo Favoni A. Ipp David I. Müller Daniel Schuh AI4CE 116 0 0 08 Nov 2021
Machine learning spectral functions in lattice QCD S.-Y. Chen H. Ding Yifan Zhang G. Papp C.-B. Yang 226 20 0 26 Oct 2021
Embedded-model flows: Combining the inductive biases of model-free deep learning and explicit probabilistic modelingInternational Conference on Learning Representations (ICLR), 2021 Gianluigi Silvestri Emily Fertig David A. Moore Luca Ambrogioni BDL TPM AI4CE 309 4 0 12 Oct 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 164 37 0 06 Oct 2021
A deep generative model for probabilistic energy forecasting in power systems: normalizing flowsApplied Energy (Appl. Energy), 2021 Jonathan Dumas Antoine Wehenkel Bertrand Cornélusse Antonio Sutera AI4TS 342 93 0 17 Jun 2021
A Probabilistic Forecast-Driven Strategy for a Risk-Aware Participation in the Capacity Firming Market: extended versionIEEE Transactions on Sustainable Energy (IEEE Trans. Sustain. Energy), 2021 Jonathan Dumas Colin Cointe Antoine Wehenkel Antonio Sutera X. Fettweis Bertrand Cornélusse 122 10 0 28 May 2021
Deep Learning Hamiltonian Monte Carlo Sam Foreman Xiao-Yong Jin James C. Osborn 86 16 0 07 May 2021