8 papers
The critical slowing down in diffusion models
Luca Maria Del Bono, Giulio Biroli, Patrick Charbonneau +1
Computational sampling has been central to the sciences since the mid-20th century. While machine-learning-based approaches have recently enabled major advances, their behavior rem…
Efficient Monte Carlo sampling of metastable systems using non-local collective variable updates
Christoph Schönle, Davide Carbone, Marylou Gabrié +2
Monte Carlo simulations are widely used to simulate complex molecular systems, but standard approaches suffer from metastability. Lately, the use of non-local proposal updates in a…
Stochastic Localization via Iterative Posterior Sampling
Louis Grenioux, Maxence Noble, Marylou Gabrié +1
Building upon score-based learning, new interest in stochastic localization techniques has recently emerged. In these models, one seeks to noise a sample from the data distribution…
Annealing in variational inference mitigates mode collapse: A theoretical study on Gaussian mixtures
Luigi Fogliani, Bruno Loureiro, Marylou Gabrié
Mode collapse, the failure to capture one or more modes when targetting a multimodal distribution, is a central challenge in modern variational inference. In this work, we provide…
Boltzmann generators for amorphous particle systems
Louis Grenioux, Leonardo Galliano, Ludovic Berthier +2
Sampling configurations in thermodynamic equilibrium is a long-standing challenge in statistical physics. Boltzmann generators address this problem by employing generative models t…
Modern applications of machine learning in quantum sciences
Anna Dawid, Julian Arnold, Borja Requena +26
In this book, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover the use of deep learn…