1.3k citations · 3.2k across the 74 of their papers we have counts for
78 papers
Computing equilibrium free energies through a nonequilibrium quench
Kangxin Liu, Grant M. Rotskoff, Eric Vanden-Eijnden +1
Many methods to accelerate sampling of molecular configurations are based on the idea that temperature can be used to accelerate rare transitions. These methods typically compute e…
Coupling parameter and particle dynamics for adaptive sampling in Neural Galerkin schemes
Yuxiao Wen, Eric Vanden-Eijnden, Benjamin Peherstorfer
Training nonlinear parametrizations such as deep neural networks to numerically approximate solutions of partial differential equations is often based on minimizing a loss that inc…
Efficient Training of Energy-Based Models Using Jarzynski Equality
Davide Carbone, Mengjian Hua, Simon Coste +1
Energy-based models (EBMs) are generative models inspired by statistical physics with a wide range of applications in unsupervised learning. Their performance is best measured by t…
Stochastic Interpolants: A Unifying Framework for Flows and Diffusions
Michael S. Albergo, Nicholas M. Boffi, Eric Vanden-Eijnden
A class of generative models that unifies flow-based and diffusion-based methods is introduced. These models extend the framework proposed in Albergo and Vanden-Eijnden (2023), ena…
A Functional-Space Mean-Field Theory of Partially-Trained Three-Layer Neural Networks
Zhengdao Chen, Eric Vanden-Eijnden, Joan Bruna
To understand the training dynamics of neural networks, prior studies have considered the mean-field limit of two-layer neural networks as the width tends to infinity, establishing…
Building Normalizing Flows with Stochastic Interpolants
Michael S. Albergo, Eric Vanden-Eijnden
A generative model based on a continuous-time normalizing flow between any pair of base and target probability densities is proposed. The velocity field of this flow is inferred fr…