arXiv Machine Learning July 24, 2026 By Janik Kreit, Dominic Schuh, Kim A. Nicoli, Lena Funcke

SESaMo: Symmetry-Enforcing Stochastic Modulation for Normalizing Flows

Read the original on arXiv Machine Learning →

arXiv:2505. 19619v3 Announce Type: replace Abstract: Deep generative models have recently garnered significant attention across various fields, from physics to chemistry, where sampling from unnormalized Boltzmann-like distributions represents a fundamental challenge.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

benchmarks

arXiv Machine Learning

Jul 24

Boltzmann generators for amorphous particle systems

arXiv:2512. 16607v2 Announce Type: replace-cross Abstract: Sampling configurations in thermodynamic equilibrium is a long-standing challenge in statistical physics.

By Louis Grenioux, Leonardo Galliano, Ludovic Berthier, Giulio Biroli, Marylou Gabri\'e

diffusion

arXiv Machine Learning

Jun 26

Equivariance and Augmentation for Bayesian Neural Networks

arXiv:2606. 26273v1 Announce Type: new Abstract: Symmetries are important for many deep learning tasks, ranging from applications in the sciences to medical imaging.

By Miaowen Dong, Axel Flinth, Jan E. Gerken

arXiv Machine Learning

Jun 24

Scalable Physics-Inspired Transformers for Spin Glasses

arXiv:2606. 22984v2 Announce Type: replace-cross Abstract: Efficient sampling of the Boltzmann distribution in frustrated spin glasses is central to statistical mechanics and combinatorial optimization.

By Lu Zhong, Wenli Duan, Jing Liu, Pan Zhang, Ying Tang

llms rag

arXiv Machine Learning

Aug 4

Amortized Inference of Multi-Modal Posteriors using Likelihood-Weighted Normalizing Flows

arXiv:2512. 04954v3 Announce Type: replace Abstract: We present a novel technique for amortized posterior estimation using Normalizing Flows trained with likelihood-weighted importance sampling.

By Rajneil Baruah

multimodal benchmarks

arXiv Machine Learning

Aug 11

Leveraging generative models to assist Monte Carlo sampling

arXiv:2608. 07648v1 Announce Type: cross Abstract: Sampling high-dimensional probability distributions is a central task in scientific computing, with applications ranging from Bayesian inference to statistical physics and molecular simulation.

By Marylou Gabri\'e

diffusion multimodal

arXiv Machine Learning

Jul 23

Diffusion-based Annealed Boltzmann Generators : benefits, pitfalls and hopes

arXiv:2601. 21026v2 Announce Type: replace-cross Abstract: Sampling configurations at thermodynamic equilibrium is a central challenge in statistical physics.

By Louis Grenioux, Maxence Noble

diffusion multimodal

Related stories

Boltzmann generators for amorphous particle systems

Equivariance and Augmentation for Bayesian Neural Networks

Scalable Physics-Inspired Transformers for Spin Glasses

Amortized Inference of Multi-Modal Posteriors using Likelihood-Weighted Normalizing Flows

Leveraging generative models to assist Monte Carlo sampling

Diffusion-based Annealed Boltzmann Generators : benefits, pitfalls and hopes