{"total":32,"rows":[{"id":"2103.04922","title":"Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models","created":"2021-03-08","cited_by_count":252,"area":null},{"id":"2111.15141","title":"Path Integral Sampler: a stochastic control approach for sampling","created":"2021-11-30","cited_by_count":181,"area":null},{"id":"2302.01170","title":"Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics","created":"2023-02-02","cited_by_count":66,"area":null},{"id":"2111.04726","title":"Estimating High Order Gradients of the Data Distribution by Denoising","created":"2021-11-08","cited_by_count":64,"area":null},{"id":"2109.11375","title":"Stochastic Normalizing Flows for Inverse Problems: a Markov Chains Viewpoint","created":"2021-09-23","cited_by_count":32,"area":null},{"id":"2211.07541","title":"Aspects of scaling and scalability for flow-based sampling of lattice QCD","created":"2022-11-14","cited_by_count":26,"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":"2105.05650","title":"Unbiased Monte Carlo Cluster Updates with Autoregressive Neural Networks","created":"2021-05-12","cited_by_count":17,"area":null},{"id":"2107.08001","title":"Efficient Bayesian Sampling Using Normalizing Flows to Assist Markov Chain Monte Carlo Methods","created":"2021-07-16","cited_by_count":16,"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":"2305.14442","title":"Optimal Preconditioning and Fisher Adaptive Langevin Sampling","created":"2023-05-23","cited_by_count":14,"area":null},{"id":"2310.01105","title":"Energy-Guided Continuous Entropic Barycenter Estimation for General Costs","created":"2023-10-02","cited_by_count":12,"area":null},{"id":"2109.03867","title":"LSB: Local Self-Balancing MCMC in Discrete Spaces","created":"2021-09-08","cited_by_count":7,"area":null},{"id":"2401.02080","title":"Energy based diffusion generator for efficient sampling of Boltzmann distributions","created":"2024-01-04","cited_by_count":7,"area":null},{"id":"2406.15747","title":"Modeling Unknown Stochastic Dynamical System Subject to External Excitation","created":"2024-06-22","cited_by_count":7,"area":null},{"id":"2301.13743","title":"Zero-shot-Learning Cross-Modality Data Translation Through Mutual Information Guided Stochastic Diffusion","created":"2023-01-31","cited_by_count":6,"area":null},{"id":"2111.11510","title":"Bootstrap Your Flow","created":"2021-11-22","cited_by_count":5,"area":null},{"id":"2406.12764","title":"Quasi-Bayes meets Vines","created":"2024-06-18","cited_by_count":5,"area":null},{"id":"2406.13661","title":"Hitchhiker's guide on the relation of Energy-Based Models with other generative models, sampling and statistical physics: a comprehensive review","created":"2024-06-19","cited_by_count":5,"area":null},{"id":"2304.11251","title":"Machine Learning and the Future of Bayesian Computation","created":"2023-04-21","cited_by_count":4,"area":null},{"id":"2309.13593","title":"Self-Tuning Hamiltonian Monte Carlo for Accelerated Sampling","created":"2023-09-24","cited_by_count":4,"area":null},{"id":"2101.11306","title":"Learning Non-linear Wavelet Transformation via Normalizing Flow","created":"2021-01-27","cited_by_count":3,"area":null},{"id":"2107.00352","title":"Reparameterized Sampling for Generative Adversarial Networks","created":"2021-07-01","cited_by_count":2,"area":null},{"id":"2106.02104","title":"Semi-Empirical Objective Functions for MCMC Proposal Optimization","created":"2021-06-03","cited_by_count":1,"area":null},{"id":"2406.02490","title":"Ai-Sampler: Adversarial Learning of Markov kernels with involutive maps","created":"2024-06-04","cited_by_count":1,"area":null},{"id":"2604.25710","title":"Adaptive Meta-Learning Stochastic Gradient Hamiltonian Monte Carlo Simulation for Bayesian Updating of Structural Dynamic Models","created":"2026-04-28","cited_by_count":1,"area":null},{"id":"2308.01731","title":"Quantification of Predictive Uncertainty via Inference-Time Sampling","created":"2023-08-03","cited_by_count":0,"area":null},{"id":"2606.06412","title":"Nonreversible Gauge Fields in Fokker--Planck Dynamics: Supersymmetric Hamiltonians and Learned Finite Forces","created":"2026-06-04","cited_by_count":0,"area":null},{"id":"2607.15682","title":"Neural Non-Equilibrium Hamiltonian Monte Carlo for Corrected Boltzmann Sampling","created":"2026-07-17","cited_by_count":0,"area":null},{"id":"2610.12679","title":"Amortised Inference through One-Step Implicit Sampling","created":"2026-10-08","cited_by_count":0,"area":null},{"id":"2610.19946","title":"Learning Graph Energies for Finite-Time Generation","created":"2026-10-14","cited_by_count":0,"area":null}],"next_cursor":null}