Importance Weighted Score Matching for Diffusion Samplers with Enhanced Mode Coverage

26 May 2025

Papers citing "Importance Weighted Score Matching for Diffusion Samplers with Enhanced Mode Coverage"

29 / 29 papers shown

Title
Training Neural Samplers with Reverse Diffusive KL Divergence Wenlin Chen Jiajun He Mingtian Zhang David Barber José Miguel Hernández-Lobato DiffM 94 8 0 16 Oct 2024
NETS: A Non-Equilibrium Transport Sampler M. S. Albergo Eric Vanden-Eijnden DiffM 104 21 0 03 Oct 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 72 55 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 157 26 0 07 Feb 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 69 46 0 04 Oct 2023
Nearly $d$ -Linear Convergence Bounds for Diffusion Models via Stochastic Localization Joe Benton Valentin De Bortoli Arnaud Doucet George Deligiannidis DiffM 83 114 0 07 Aug 2023
Entropy-based Training Methods for Scalable Neural Implicit Sampler Weijian Luo Boya Zhang Zhihua Zhang 65 12 0 08 Jun 2023
Rigid Body Flows for Sampling Molecular Crystal Structures Jonas Köhler Michele Invernizzi P. D. Haan Frank Noé AI4CE 74 29 0 26 Jan 2023
An optimal control perspective on diffusion-based generative modeling Julius Berner Lorenz Richter Karen Ullrich DiffM 113 92 0 02 Nov 2022
GFlowNets and variational inference Nikolay Malkin Salem Lahlou T. Deleu Xu Ji J. E. Hu Katie Everett Dinghuai Zhang Yoshua Bengio BDL 187 86 0 02 Oct 2022
Flow Annealed Importance Sampling Bootstrap Laurence Illing Midgley Vincent Stimper G. Simm Bernhard Schölkopf José Miguel Hernández-Lobato 97 91 0 03 Aug 2022
Elucidating the Design Space of Diffusion-Based Generative Models Tero Karras M. Aittala Timo Aila S. Laine DiffM 186 1,981 0 01 Jun 2022
Path Integral Sampler: a stochastic control approach for sampling Qinsheng Zhang Yongxin Chen DiffM 87 114 0 30 Nov 2021
MCMC Variational Inference via Uncorrected Hamiltonian Annealing Tomas Geffner Justin Domke 73 36 0 08 Jul 2021
Schr{ö}dinger-F{ö}llmer Sampler: Sampling without Ergodicity Jian Huang Yuling Jiao Lican Kang Xu Liao Jin Liu Yanyan Liu 74 28 0 21 Jun 2021
Score-Based Generative Modeling through Stochastic Differential Equations Yang Song Jascha Narain Sohl-Dickstein Diederik P. Kingma Abhishek Kumar Stefano Ermon Ben Poole DiffM SyDa 338 6,480 0 26 Nov 2020
Denoising Diffusion Probabilistic Models Jonathan Ho Ajay Jain Pieter Abbeel DiffM 634 18,096 0 19 Jun 2020
Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities Jonas Köhler Leon Klein Frank Noé DRL 98 271 0 03 Jun 2020
Stochastic Normalizing Flows Hao Wu Jonas Köhler Frank Noé 127 185 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 61 89 0 12 Feb 2020
Machine learning for molecular simulation Frank Noé A. Tkatchenko K. Müller C. Clementi AI4CE 73 664 0 07 Nov 2019
Variational f-divergence Minimization Mingtian Zhang Thomas Bird Raza Habib Tianlin Xu David Barber FedML 40 28 0 27 Jul 2019
Generative Modeling by Estimating Gradients of the Data Distribution Yang Song Stefano Ermon SyDa DiffM 258 3,916 0 12 Jul 2019
Sliced Score Matching: A Scalable Approach to Density and Score Estimation Yang Song Sahaj Garg Jiaxin Shi Stefano Ermon 110 417 0 17 May 2019
Flow-based generative models for Markov chain Monte Carlo in lattice field theory M. S. Albergo G. Kanwar P. Shanahan AI4CE 51 218 0 26 Apr 2019
Neural Importance Sampling Thomas Müller Brian McWilliams Fabrice Rousselle Markus Gross Jan Novák 70 364 0 11 Aug 2018
Faster Eigenvector Computation via Shift-and-Invert Preconditioning Dan Garber Laurent Dinh Chi Jin Jascha Narain Sohl-Dickstein Samy Bengio Praneeth Netrapalli Aaron Sidford 266 78 0 26 May 2016
Playing Atari with Deep Reinforcement Learning Volodymyr Mnih Koray Kavukcuoglu David Silver Alex Graves Ioannis Antonoglou Daan Wierstra Martin Riedmiller 127 12,231 0 19 Dec 2013
The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo Matthew D. Hoffman Andrew Gelman 165 4,302 0 18 Nov 2011