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Learned Reference-based Diffusion Sampling for multi-modal distributions

25 October 2024

Papers citing "Learned Reference-based Diffusion Sampling for multi-modal distributions"

46 / 46 papers shown

Title
Improving the evaluation of samplers on multi-modal targets Louis Grenioux Maxence Noble Marylou Gabrié 433 0 0 11 Apr 2025
End-To-End Learning of Gaussian Mixture Priors for Diffusion Sampler Denis Blessing Xiaogang Jia Gerhard Neumann DiffM 90 1 0 01 Mar 2025
Beyond ELBOs: A Large-Scale Evaluation of Variational Methods for Sampling Denis Blessing Xiaogang Jia Johannes Esslinger Francisco Vargas Gerhard Neumann 101 26 0 11 Jun 2024
Stochastic Localization via Iterative Posterior Sampling Louis Grenioux Maxence Noble Marylou Gabrié Alain Durmus DiffM 76 16 0 16 Feb 2024
Target Score Matching Valentin De Bortoli M. Hutchinson Peter Wirnsberger Arnaud Doucet DiffM 76 20 0 13 Feb 2024
Particle Denoising Diffusion Sampler Angus Phillips Hai-Dang Dau M. Hutchinson Valentin De Bortoli George Deligiannidis Arnaud Doucet DiffM 103 30 0 09 Feb 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 74 57 0 09 Feb 2024
Faster Sampling without Isoperimetry via Diffusion-based Monte Carlo Xunpeng Huang Difan Zou Hanze Dong Yian Ma Tong Zhang DiffM 52 12 0 12 Jan 2024
Learning Energy-Based Models by Cooperative Diffusion Recovery Likelihood Y. Zhu Jianwen Xie Yingnian Wu Ruiqi Gao DiffM 101 14 0 10 Sep 2023
Reverse Diffusion Monte Carlo Xunpeng Huang Hanze Dong Yi Hao Yi-An Ma Tong Zhang DiffM 70 28 0 05 Jul 2023
Improved sampling via learned diffusions Lorenz Richter Julius Berner DiffM 94 63 0 03 Jul 2023
Persistently Trained, Diffusion-assisted Energy-based Models Xinwei Zhang Z. Tan Zhijian Ou DiffM 64 2 0 21 Apr 2023
Denoising Diffusion Samplers Francisco Vargas Will Grathwohl Arnaud Doucet DiffM 56 89 0 27 Feb 2023
On Sampling with Approximate Transport Maps Louis Grenioux Alain Durmus Eric Moulines Marylou Gabrié OT 31 17 0 09 Feb 2023
An optimal control perspective on diffusion-based generative modeling Julius Berner Lorenz Richter Karen Ullrich DiffM 127 94 0 02 Nov 2022
Denoising MCMC for Accelerating Diffusion-Based Generative Models Beomsu Kim Jong Chul Ye DiffM 76 15 0 29 Sep 2022
A Unified Perspective on Natural Gradient Variational Inference with Gaussian Mixture Models Oleg Arenz Philipp Dahlinger Zihan Ye Michael Volpp Gerhard Neumann 97 17 0 23 Sep 2022
Langevin Diffusion Variational Inference Tomas Geffner Justin Domke DiffM 44 24 0 16 Aug 2022
Score-Based Diffusion meets Annealed Importance Sampling Arnaud Doucet Will Grathwohl A. G. Matthews Heiko Strathmann DiffM 91 46 0 16 Aug 2022
Flow Annealed Importance Sampling Bootstrap Laurence Illing Midgley Vincent Stimper G. Simm Bernhard Schölkopf José Miguel Hernández-Lobato 102 94 0 03 Aug 2022
Elucidating the Design Space of Diffusion-Based Generative Models Tero Karras M. Aittala Timo Aila S. Laine DiffM 215 2,022 0 01 Jun 2022
Continual Repeated Annealed Flow Transport Monte Carlo A. G. Matthews Michael Arbel Danilo Jimenez Rezende Arnaud Doucet OT 94 53 0 31 Jan 2022
Path Integral Sampler: a stochastic control approach for sampling Qinsheng Zhang Yongxin Chen DiffM 100 117 0 30 Nov 2021
Bayesian Learning via Neural Schrödinger-Föllmer Flows Francisco Vargas Andrius Ovsianas David Fernandes Mark Girolami Neil D. Lawrence Nikolas Nusken BDL 87 49 0 20 Nov 2021
MCMC Variational Inference via Uncorrected Hamiltonian Annealing Tomas Geffner Justin Domke 75 36 0 08 Jul 2021
Variational Refinement for Importance Sampling Using the Forward Kullback-Leibler Divergence Ghassen Jerfel S. Wang Clara Fannjiang Katherine A. Heller Yi-An Ma Michael I. Jordan BDL 125 40 0 30 Jun 2021
Diffusion Models Beat GANs on Image Synthesis Prafulla Dhariwal Alex Nichol 265 7,938 0 11 May 2021
Improved Denoising Diffusion Probabilistic Models Alex Nichol Prafulla Dhariwal DiffM 352 3,715 0 18 Feb 2021
Annealed Flow Transport Monte Carlo Michael Arbel A. G. Matthews Arnaud Doucet 83 78 0 15 Feb 2021
Learning Energy-Based Models by Diffusion Recovery Likelihood Ruiqi Gao Yang Song Ben Poole Ying Nian Wu Diederik P. Kingma DiffM 64 128 0 15 Dec 2020
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 353 6,566 0 26 Nov 2020
Denoising Diffusion Probabilistic Models Jonathan Ho Ajay Jain Pieter Abbeel DiffM 689 18,310 0 19 Jun 2020
Improved Techniques for Training Score-Based Generative Models Yang Song Stefano Ermon DiffM 260 1,163 0 16 Jun 2020
Solving high-dimensional Hamilton-Jacobi-Bellman PDEs using neural networks: perspectives from the theory of controlled diffusions and measures on path space Nikolas Nusken Lorenz Richter AI4CE 71 111 0 11 May 2020
Stochastic Normalizing Flows Hao Wu Jonas Köhler Frank Noé 131 185 0 16 Feb 2020
Normalizing Flows for Probabilistic Modeling and Inference George Papamakarios Eric T. Nalisnick Danilo Jimenez Rezende S. Mohamed Balaji Lakshminarayanan TPM AI4CE 209 1,713 0 05 Dec 2019
Generative Modeling by Estimating Gradients of the Data Distribution Yang Song Stefano Ermon SyDa DiffM 258 3,956 0 12 Jul 2019
Non-Reversible Parallel Tempering: a Scalable Highly Parallel MCMC Scheme Saifuddin Syed Alexandre Bouchard-Côté George Deligiannidis Arnaud Doucet 72 73 0 08 May 2019
Theoretical guarantees for sampling and inference in generative models with latent diffusions Belinda Tzen Maxim Raginsky DiffM 66 101 0 05 Mar 2019
A Framework for Adaptive MCMC Targeting Multimodal Distributions E. Pompe Chris Holmes K. Latuszyñski 78 59 0 06 Dec 2018
Weight-Preserving Simulated Tempering Nicholas G. Tawn Gareth O. Roberts Jeffrey S. Rosenthal 65 28 0 14 Aug 2018
Non-asymptotic convergence analysis for the Unadjusted Langevin Algorithm Alain Durmus Eric Moulines 69 415 0 17 Jul 2015
Deep Unsupervised Learning using Nonequilibrium Thermodynamics Jascha Narain Sohl-Dickstein Eric A. Weiss Niru Maheswaranathan Surya Ganguli SyDa DiffM 306 7,016 0 12 Mar 2015
Theoretical guarantees for approximate sampling from smooth and log-concave densities A. Dalalyan 83 516 0 23 Dec 2014
MCMC using Hamiltonian dynamics Radford M. Neal 292 3,282 0 09 Jun 2012
The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo Matthew D. Hoffman Andrew Gelman 169 4,309 0 18 Nov 2011