{"total":23,"rows":[{"id":"2201.08862","title":"Stochastic normalizing flows as non-equilibrium transformations","created":"2022-01-21","cited_by_count":25,"area":null},{"id":"2110.02673","title":"Scaling Up Machine Learning For Quantum Field Theory with Equivariant Continuous Flows","created":"2021-10-06","cited_by_count":24,"area":null},{"id":"2207.08219","title":"Gradients should stay on Path: Better Estimators of the Reverse- and Forward KL Divergence for Normalizing Flows","created":"2022-07-17","cited_by_count":23,"area":null},{"id":"2309.01156","title":"Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics","created":"2023-09-03","cited_by_count":23,"area":null},{"id":"2111.02702","title":"Local-Global MCMC kernels: the best of both worlds","created":"2021-11-04","cited_by_count":22,"area":null},{"id":"2302.04763","title":"On Sampling with Approximate Transport Maps","created":"2023-02-09","cited_by_count":21,"area":null},{"id":"2202.05838","title":"Applications of Machine Learning to Lattice Quantum Field Theory","created":"2022-02-10","cited_by_count":14,"area":null},{"id":"2307.01107","title":"Sampling the lattice Nambu-Goto string using Continuous Normalizing Flows","created":"2023-07-03","cited_by_count":12,"area":null},{"id":"2111.10189","title":"Analysis of autocorrelation times in Neural Markov Chain Monte Carlo simulations","created":"2021-11-19","cited_by_count":10,"area":null},{"id":"2111.11303","title":"Machine Learning of Thermodynamic Observables in the Presence of Mode Collapse","created":"2021-11-22","cited_by_count":10,"area":null},{"id":"2401.15948","title":"AdvNF: Reducing Mode Collapse in Conditional Normalising Flows using Adversarial Learning","created":"2024-01-29","cited_by_count":6,"area":null},{"id":"2404.11674","title":"Practical applications of machine-learned flows on gauge fields","created":"2024-04-17","cited_by_count":6,"area":null},{"id":"2112.15532","title":"Machine Learning Trivializing Maps: A First Step Towards Understanding How Flow-Based Samplers Scale Up","created":"2021-12-31","cited_by_count":5,"area":null},{"id":"2212.04955","title":"Simulating first-order phase transition with hierarchical autoregressive networks","created":"2022-12-09","cited_by_count":5,"area":null},{"id":"2112.02309","title":"Machine Learning in Nuclear Physics","created":"2021-12-04","cited_by_count":4,"area":null},{"id":"2306.00684","title":"Balanced Training of Energy-Based Models with Adaptive Flow Sampling","created":"2023-06-01","cited_by_count":4,"area":null},{"id":"2310.11232","title":"Learning to Sample Better","created":"2023-10-17","cited_by_count":4,"area":null},{"id":"2309.09743","title":"The NFLikelihood: an unsupervised DNNLikelihood from Normalizing Flows","created":"2023-09-18","cited_by_count":2,"area":null},{"id":"2203.14824","title":"Numerical and geometrical aspects of flow-based variational quantum Monte Carlo","created":"2022-03-28","cited_by_count":1,"area":null},{"id":"2503.11482","title":"NeuMC -- a package for neural sampling for lattice field theories","created":"2025-03-14","cited_by_count":1,"area":null},{"id":"2304.01798","title":"Locality-constrained autoregressive cum conditional normalizing flow for lattice field theory simulations","created":"2023-04-04","cited_by_count":0,"area":null},{"id":"2403.18840","title":"An AI-powered Technology Stack for Solving Many-Electron Field Theory","created":"2024-02-28","cited_by_count":0,"area":null},{"id":"2605.11199","title":"Operator-Guided Model Reduction for Generative Sampling in Lattice Field Theory","created":"2026-05-11","cited_by_count":0,"area":null}],"next_cursor":null}