Stochastic Normalizing Flows

16 February 2020

Frank Noé

Papers citing "Stochastic Normalizing Flows"

50 / 122 papers shown

Title
Ergodic Generative Flows Leo Maxime Brunswic Mateo Clemente Rui Heng Yang Adam Sigal Amir Rasouli Yinchuan Li 37 0 0 06 May 2025
Diffusion-based supervised learning of generative models for efficient sampling of multimodal distributions Hoang Tran Zezhong Zhang F. Bao Dan Lu Guannan Zhang DiffM 42 0 0 20 Apr 2025
Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching Aaron J. Havens Benjamin Kurt Miller Bing Yan Carles Domingo-Enrich Anuroop Sriram ... Brandon Amos Brian Karrer Xiang Fu Guan-Horng Liu Ricky T. Q. Chen DiffM 43 0 0 16 Apr 2025
Underdamped Diffusion Bridges with Applications to Sampling Denis Blessing Julius Berner Lorenz Richter Gerhard Neumann DiffM 34 1 0 02 Mar 2025
End-To-End Learning of Gaussian Mixture Priors for Diffusion Sampler Denis Blessing Xiaogang Jia Gerhard Neumann DiffM 43 0 0 01 Mar 2025
Single-Step Consistent Diffusion Samplers Pascal Jutras-Dubé Patrick Pynadath Ruqi Zhang DiffM 73 0 0 17 Feb 2025
Neural Flow Samplers with Shortcut Models Wuhao Chen Zijing Ou Yingzhen Li 77 0 0 11 Feb 2025
Physics-Conditioned Diffusion Models for Lattice Gauge Theory Qianteng Zhu Gert Aarts Wei Wang K. Zhou L. Wang 53 1 0 08 Feb 2025
Empirical evaluation of normalizing flows in Markov Chain Monte Carlo David Nabergoj Erik Štrumbelj BDL TPM 36 0 0 22 Dec 2024
Diffusion Model from Scratch Wang Zhen Dong Yunyun DiffM 59 0 0 14 Dec 2024
Sampling from Boltzmann densities with physics informed low-rank formats Paul Hagemann Janina Enrica Schutte David Sommer Martin Eigel Gabriele Steidl 71 0 0 10 Dec 2024
Denoising Fisher Training For Neural Implicit Samplers Weijian Luo Wei Deng 23 0 0 03 Nov 2024
On learning higher-order cumulants in diffusion models Gert Aarts Diaa E. Habibi L. Wang K. Zhou 26 4 0 28 Oct 2024
Learned Reference-based Diffusion Sampling for multi-modal distributions Maxence Noble Louis Grenioux Marylou Gabrié Alain Durmus DiffM 29 2 0 25 Oct 2024
Training Neural Samplers with Reverse Diffusive KL Divergence Jiajun He Wenlin Chen Mingtian Zhang David Barber José Miguel Hernández-Lobato DiffM 26 4 0 16 Oct 2024
Provable Convergence and Limitations of Geometric Tempering for Langevin Dynamics Omar Chehab Anna Korba Austin Stromme Adrien Vacher 33 2 0 13 Oct 2024
Numerical determination of the width and shape of the effective string using Stochastic Normalizing Flows M. Caselle E. Cellini A. Nada 23 4 0 24 Sep 2024
Variational Learning of Gaussian Process Latent Variable Models through Stochastic Gradient Annealed Importance Sampling Jian Xu Shian Du Junmei Yang Qianli Ma Delu Zeng BDL 19 0 0 13 Aug 2024
Importance Corrected Neural JKO Sampling Johannes Hertrich Robert Gruhlke 26 1 0 29 Jul 2024
Combining Wasserstein-1 and Wasserstein-2 proximals: robust manifold learning via well-posed generative flows Hyemin Gu M. Katsoulakis Luc Rey-Bellet Benjamin J. Zhang 32 2 0 16 Jul 2024
Dynamical Measure Transport and Neural PDE Solvers for Sampling Jingtong Sun Julius Berner Lorenz Richter Marius Zeinhofer Johannes Müller Kamyar Azizzadenesheli Anima Anandkumar OT DiffM 34 8 0 10 Jul 2024
Transferable Boltzmann Generators Leon Klein Frank Noé 35 12 0 20 Jun 2024
Beyond ELBOs: A Large-Scale Evaluation of Variational Methods for Sampling Denis Blessing Xiaogang Jia Johannes Esslinger Francisco Vargas Gerhard Neumann 50 16 0 11 Jun 2024
RecMoDiffuse: Recurrent Flow Diffusion for Human Motion Generation Mirgahney Mohamed Harry Jake Cunningham M. Deisenroth Lourdes Agapito DiffM 33 0 0 11 Jun 2024
A Diffusion Model Framework for Unsupervised Neural Combinatorial Optimization Sebastian Sanokowski Sepp Hochreiter Sebastian Lehner 27 17 0 03 Jun 2024
Markovian Flow Matching: Accelerating MCMC with Continuous Normalizing Flows A. Cabezas Louis Sharrock Christopher Nemeth 34 1 0 23 May 2024
Expensive Multi-Objective Bayesian Optimization Based on Diffusion Models Bingdong Li Zixiang Di Yongfan Lu Hong Qian Feng Wang Peng Yang Ke Tang Aimin Zhou DiffM 22 1 0 14 May 2024
Liouville Flow Importance Sampler Yifeng Tian Nishant Panda Yen Ting Lin 28 8 0 03 May 2024
Practical applications of machine-learned flows on gauge fields Ryan Abbott M. S. Albergo D. Boyda D. Hackett G. Kanwar Fernando Romero-López P. Shanahan Julian M. Urban AI4CE 22 11 0 17 Apr 2024
Nonparametric Automatic Differentiation Variational Inference with Spline Approximation Yuda Shao Shan Yu Tianshu Feng 16 1 0 10 Mar 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 32 41 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 64 17 0 07 Feb 2024
Diffusive Gibbs Sampling Wenlin Chen Mingtian Zhang Brooks Paige José Miguel Hernández-Lobato David Barber 12 7 0 05 Feb 2024
Mixed Noise and Posterior Estimation with Conditional DeepGEM Paul Hagemann J. Hertrich Maren Casfor Sebastian Heidenreich Gabriele Steidl 14 0 0 05 Feb 2024
AdvNF: Reducing Mode Collapse in Conditional Normalising Flows using Adversarial Learning V. Kanaujia Mathias S. Scheurer Vipul Arora GAN DRL 12 2 0 29 Jan 2024
Scalable Normalizing Flows Enable Boltzmann Generators for Macromolecules Joseph C. Kim David Bloore Karan Kapoor Jun Feng Ming-Hong Hao Mengdi Wang 37 7 0 08 Jan 2024
Energy based diffusion generator for efficient sampling of Boltzmann distributions Yan Wang Ling Guo Hao Wu Tao Zhou DiffM 31 3 0 04 Jan 2024
Learning from small data sets: Patch-based regularizers in inverse problems for image reconstruction Moritz Piening Fabian Altekrüger J. Hertrich Paul Hagemann Andrea Walther Gabriele Steidl 24 6 0 27 Dec 2023
On the Quantification of Image Reconstruction Uncertainty without Training Data Sirui Bi Victor Fung Jiaxin Zhang 8 1 0 16 Nov 2023
Topology-Matching Normalizing Flows for Out-of-Distribution Detection in Robot Learning Jianxiang Feng Jongseok Lee Simon Geisler Stephan Gunnemann Rudolph Triebel OODD 19 4 0 11 Nov 2023
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 24 40 0 04 Oct 2023
Neural Stochastic Differential Equations for Robust and Explainable Analysis of Electromagnetic Unintended Radiated Emissions Sumit Kumar Jha Susmit Jha Rickard Ewetz Alvaro Velasquez 10 2 0 27 Sep 2023
Variations and Relaxations of Normalizing Flows Keegan Kelly Lorena Piedras Sukrit Rao David Samuel Roth BDL 25 0 0 08 Sep 2023
Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics Kyle Cranmer G. Kanwar S. Racanière Danilo Jimenez Rezende P. Shanahan AI4CE 18 27 0 03 Sep 2023
Learning variational autoencoders via MCMC speed measures Marcel Hirt Vasileios Kreouzis P. Dellaportas BDL DRL 13 2 0 26 Aug 2023
SE(3) Equivariant Augmented Coupling Flows Laurence I. Midgley Vincent Stimper Javier Antorán Emile Mathieu Bernhard Schölkopf José Miguel Hernández-Lobato 30 22 0 20 Aug 2023
Fast Inference and Update of Probabilistic Density Estimation on Trajectory Prediction Takahiro Maeda Norimichi Ukita 21 27 0 17 Aug 2023
A Review of Change of Variable Formulas for Generative Modeling Ullrich Kothe 19 6 0 04 Aug 2023
Improved sampling via learned diffusions Lorenz Richter Julius Berner DiffM 24 52 0 03 Jul 2023
Transport meets Variational Inference: Controlled Monte Carlo Diffusions Francisco Vargas Shreyas Padhy Denis Blessing Nikolas Nusken DiffM OT 40 3 0 03 Jul 2023