Eric Vanden-Eijnden | DeepAI

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Featured Co-authors

Jianfeng Lu
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∙ 06/27/2023

Coupling parameter and particle dynamics for adaptive sampling in Neural Galerkin schemes

Training nonlinear parametrizations such as deep neural networks to nume...

0 Yuxiao Wen, et al. ∙

∙ 05/30/2023

Efficient Training of Energy-Based Models Using Jarzynski Equality

Energy-based models (EBMs) are generative models inspired by statistical...

0 Davide Carbone, et al. ∙

∙ 03/15/2023

Stochastic Interpolants: A Unifying Framework for Flows and Diffusions

We introduce a class of generative models based on the stochastic interp...

0 Michael S. Albergo, et al. ∙

∙ 10/28/2022

A Functional-Space Mean-Field Theory of Partially-Trained Three-Layer Neural Networks

To understand the training dynamics of neural networks (NNs), prior stud...

0 Zhengdao Chen, et al. ∙

∙ 09/30/2022

Building Normalizing Flows with Stochastic Interpolants

A simple generative model based on a continuous-time normalizing flow be...

0 Michael S. Albergo, et al. ∙

∙ 06/24/2022

Learning sparse features can lead to overfitting in neural networks

It is widely believed that the success of deep networks lies in their ab...

31 Leonardo Petrini, et al. ∙

∙ 06/20/2022

Learning Optimal Flows for Non-Equilibrium Importance Sampling

Many applications in computational sciences and statistical inference re...

11 Yu Cao, et al. ∙

∙ 06/09/2022

Probability flow solution of the Fokker-Planck equation

The method of choice for integrating the time-dependent Fokker-Planck eq...

33 Nicholas M. Boffi, et al. ∙

∙ 04/22/2022

On Feature Learning in Neural Networks with Global Convergence Guarantees

We study the optimization of wide neural networks (NNs) via gradient flo...

5 Zhengdao Chen, et al. ∙

∙ 03/02/2022

Neural Galerkin Scheme with Active Learning for High-Dimensional Evolution Equations

Machine learning methods have been shown to give accurate predictions in...

9 Joan Bruna, et al. ∙

∙ 11/29/2021

Estimating earthquake-induced tsunami inundation probabilities without sampling

Given a distribution of earthquake-induced seafloor elevations, we prese...

0 Shanyin Tong, et al. ∙

∙ 07/16/2021

Efficient Bayesian Sampling Using Normalizing Flows to Assist Markov Chain Monte Carlo Methods

Normalizing flows can generate complex target distributions and thus sho...

10 Marylou Gabrié, et al. ∙

∙ 07/11/2021

Dual Training of Energy-Based Models with Overparametrized Shallow Neural Networks

Energy-based models (EBMs) are generative models that are usually traine...

6 Carles Domingo Enrich, et al. ∙

∙ 04/15/2021

On Energy-Based Models with Overparametrized Shallow Neural Networks

Energy-based models (EBMs) are a simple yet powerful framework for gener...

15 Carles Domingo Enrich, et al. ∙

∙ 08/21/2020

A Dynamical Central Limit Theorem for Shallow Neural Networks

Recent theoretical work has characterized the dynamics of wide shallow n...

8 Zhengdao Chen, et al. ∙

∙ 08/11/2020

Learning with rare data: Using active importance sampling to optimize objectives dominated by rare events

Deep neural networks, when optimized with sufficient data, provide accur...

59 Grant M. Rotskoff, et al. ∙

∙ 07/28/2020

Extreme event probability estimation using PDE-constrained optimization and large deviation theory, with application to tsunamis

We propose and compare methods for the estimation of extreme event proba...

0 Shanyin Tong, et al. ∙

∙ 06/27/2020

Optimization and Generalization of Shallow Neural Networks with Quadratic Activation Functions

We study the dynamics of optimization and the generalization properties ...

7 Stefano Sarao Mannelli, et al. ∙

∙ 02/05/2019

Global convergence of neuron birth-death dynamics

Neural networks with a large number of parameters admit a mean-field des...

4 Grant Rotskoff, et al. ∙

∙ 09/13/2018

Simulated Tempering Method in the Infinite Switch Limit with Adaptive Weight Learning

We investigate the theoretical foundations of the simulated tempering me...

0 Anton Martinsson, et al. ∙

∙ 05/02/2018

Neural networks as Interacting Particle Systems: Asymptotic convexity of the Loss Landscape and Universal Scaling of the Approximation Error

Neural networks, a central tool in machine learning, have demonstrated r...

0 Grant M. Rotskoff, et al. ∙

∙ 12/19/2017

Methodological and computational aspects of parallel tempering methods in the infinite swapping limit

A variant of the parallel tempering method is proposed in terms of a sto...

0 Jianfeng Lu, et al. ∙

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