Flow Annealed Importance Sampling Bootstrap

3 August 2022

Laurence Illing Midgley

Vincent Stimper

G. Simm

Bernhard Schölkopf

José Miguel Hernández-Lobato

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Papers citing "Flow Annealed Importance Sampling Bootstrap"

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Title
Path Gradients after Flow Matching Lorenz Vaitl Leon Klein 5 0 0 15 May 2025
Energy-Based Coarse-Graining in Molecular Dynamics: A Flow-Based Framework Without Data Maximilian Stupp P. S. Koutsourelakis 40 0 0 29 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
Potential Score Matching: Debiasing Molecular Structure Sampling with Potential Energy Guidance Liya Guo Zun Wang Chang-Shu Liu J. Li Pipi Hu Yi Zhu DiffM 43 0 0 18 Mar 2025
On the Generalization Properties of Diffusion Models Puheng Li Zhong Li Huishuai Zhang Jiang Bian 64 29 0 13 Mar 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
Value Gradient Sampler: Sampling as Sequential Decision Making Sangwoong Yoon Himchan Hwang Hyeokju Jeong Dong Kyu Shin Che-Sang Park Sehee Kwon Frank C. Park 69 0 0 18 Feb 2025
Neural Flow Samplers with Shortcut Models Wuhao Chen Zijing Ou Yingzhen Li 77 0 0 11 Feb 2025
From discrete-time policies to continuous-time diffusion samplers: Asymptotic equivalences and faster training Julius Berner Lorenz Richter Marcin Sendera Jarrid Rector-Brooks Nikolay Malkin OffRL 56 3 0 10 Jan 2025
Empirical evaluation of normalizing flows in Markov Chain Monte Carlo David Nabergoj Erik Štrumbelj BDL TPM 36 0 0 22 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
Alpha Entropy Search for New Information-based Bayesian Optimization Daniel Fernández-Sánchez Eduardo C. Garrido-Merchán Daniel Hernández-Lobato 59 1 0 25 Nov 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
Physical Consistency Bridges Heterogeneous Data in Molecular Multi-Task Learning Yuxuan Ren Dihan Zheng Chang-Shu Liu Peiran Jin Yu Shi Lin Huang Jiyan He Shengjie Luo Tao Qin Tie-Yan Liu AI4CE 30 1 0 14 Oct 2024
NETS: A Non-Equilibrium Transport Sampler M. S. Albergo Eric Vanden-Eijnden DiffM 43 9 0 03 Oct 2024
Generative Modeling of Molecular Dynamics Trajectories Bowen Jing Hannes Stärk Tommi Jaakkola Bonnie Berger AI4CE 32 14 0 26 Sep 2024
Iterated Energy-based Flow Matching for Sampling from Boltzmann Densities Dongyeop Woo Sungsoo Ahn 30 5 0 29 Aug 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
A Practical Diffusion Path for Sampling Omar Chehab Anna Korba DiffM 29 1 0 20 Jun 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
Markovian Flow Matching: Accelerating MCMC with Continuous Normalizing Flows A. Cabezas Louis Sharrock Christopher Nemeth 34 1 0 23 May 2024
Particle Denoising Diffusion Sampler Angus Phillips Hai-Dang Dau M. Hutchinson Valentin De Bortoli George Deligiannidis Arnaud Doucet DiffM 54 25 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 32 41 0 09 Feb 2024
AlphaFold Meets Flow Matching for Generating Protein Ensembles Bowen Jing Bonnie Berger Tommi Jaakkola AI4CE 14 91 0 07 Feb 2024
PQMass: Probabilistic Assessment of the Quality of Generative Models using Probability Mass Estimation Pablo Lemos Sammy N. Sharief Nikolay Malkin Laurence Perreault Levasseur Y. Hezaveh Laurence Perreault-Levasseur Yashar Hezaveh 19 3 0 06 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
Conditional Normalizing Flows for Active Learning of Coarse-Grained Molecular Representations Henrik Schopmans Pascal Friederich AI4CE 20 1 0 02 Feb 2024
Ensemble-Based Annealed Importance Sampling Haoxuan Chen Lexing Ying 25 2 0 28 Jan 2024
Combining Normalizing Flows and Quasi-Monte Carlo Charly Andral BDL 27 1 0 11 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
Transition Path Sampling with Boltzmann Generator-based MCMC Moves Michael Plainer Hannes Stärk Charlotte Bunne Stephan Günnemann 13 5 0 08 Dec 2023
Improving Gradient-guided Nested Sampling for Posterior Inference Pablo Lemos Nikolay Malkin Will Handley Yoshua Bengio Y. Hezaveh Laurence Perreault Levasseur BDL 31 9 0 06 Dec 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
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
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
Equivariant flow matching Leon Klein Andreas Krämer Frank Noé 16 59 0 26 Jun 2023
Enhanced Sampling with Machine Learning: A Review S. Mehdi Zachary Smith Lukas Herron Ziyue Zou P. Tiwary AI4CE 11 8 0 15 Jun 2023
Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning Shuxin Zheng Jiyan He Chang-Shu Liu Yu Shi Ziheng Lu ... Peiran Jin Chi Chen Frank Noé Haiguang Liu Tie-Yan Liu AI4CE 15 40 0 08 Jun 2023
Efficient Training of Energy-Based Models Using Jarzynski Equality D. Carbone Mengjian Hua Simon Coste Eric Vanden-Eijnden 8 4 0 30 May 2023
Precision-Recall Divergence Optimization for Generative Modeling with GANs and Normalizing Flows Alexandre Verine Benjamin Négrevergne Muni Sreenivas Pydi Y. Chevaleyre 19 10 0 30 May 2023
Estimating Gibbs free energies via isobaric-isothermal flows Peter Wirnsberger Borja Ibarz George Papamakarios 14 11 0 22 May 2023
Mutual information of spin systems from autoregressive neural networks P. Białas P. Korcyl T. Stebel 9 3 0 26 Apr 2023
Comparative Study of Coupling and Autoregressive Flows through Robust Statistical Tests A. Coccaro Marco Letizia H. Reyes-González Riccardo Torre OOD 22 5 0 23 Feb 2023
On Sampling with Approximate Transport Maps Louis Grenioux Alain Durmus Eric Moulines Marylou Gabrié OT 22 15 0 09 Feb 2023
Training Normalizing Flows with the Precision-Recall Divergence Alexandre Verine Benjamin Négrevergne Muni Sreenivas Pydi Y. Chevaleyre 24 1 0 01 Feb 2023