Eric Vanden-Eijnden

Benjamin Peherstorfer

J. Comput. Phys., January, 2024

Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes.

[BibT_eX]

[DOI]

CoRR, 2024

SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers.

[BibT_eX]

[DOI]

CoRR, 2024

2023

Probability flow solution of the Fokker-Planck equation.

[BibT_eX]

[DOI]

Nicholas M. Boffi

Mach. Learn. Sci. Technol., September, 2023

Learning to Sample Better.

[BibT_eX]

[DOI]

Michael S. Albergo

CoRR, 2023

Stochastic interpolants with data-dependent couplings.

[BibT_eX]

[DOI]

CoRR, 2023

Multimarginal generative modeling with stochastic interpolants.

[BibT_eX]

[DOI]

CoRR, 2023

Analysis of learning a flow-based generative model from limited sample complexity.

[BibT_eX]

[DOI]

CoRR, 2023

Deep learning probability flows and entropy production rates in active matter.

[BibT_eX]

[DOI]

Nicholas M. Boffi

CoRR, 2023

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

[BibT_eX]

[DOI]

Yuxiao Wen

Benjamin Peherstorfer

CoRR, 2023

Stochastic Interpolants: A Unifying Framework for Flows and Diffusions.

[BibT_eX]

[DOI]

Michael S. Albergo

Nicholas M. Boffi

CoRR, 2023

Efficient Training of Energy-Based Models Using Jarzynski Equality.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Building Normalizing Flows with Stochastic Interpolants.

[BibT_eX]

[DOI]

Michael S. Albergo

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

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

[BibT_eX]

[DOI]

Zhengdao Chen

CoRR, 2022

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

[BibT_eX]

[DOI]

Benjamin Peherstorfer

CoRR, 2022

Learning sparse features can lead to overfitting in neural networks.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Learning Optimal Flows for Non-Equilibrium Importance Sampling.

[BibT_eX]

[DOI]

Yu Cao

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

On feature learning in neural networks with global convergence guarantees.

[BibT_eX]

[DOI]

Zhengdao Chen

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Estimating earthquake-induced tsunami inundation probabilities without sampling.

[BibT_eX]

[DOI]

Shanyin Tong

Georg Stadler

CoRR, 2021

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

[BibT_eX]

[DOI]

Marylou Gabrié

CoRR, 2021

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

[BibT_eX]

[DOI]

Carles Domingo-Enrich

CoRR, 2021

Active Importance Sampling for Variational Objectives Dominated by Rare Events: Consequences for Optimization and Generalization.

[BibT_eX]

[DOI]

Andrew R. Mitchell

Proceedings of the Mathematical and Scientific Machine Learning, 2021

On Energy-Based Models with Overparametrized Shallow Neural Networks.

[BibT_eX]

[DOI]

Carles Domingo-Enrich

Alberto Bietti

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

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

[BibT_eX]

[DOI]

Shanyin Tong

Georg Stadler

CoRR, 2020

Optimization and Generalization of Shallow Neural Networks with Quadratic Activation Functions.

[BibT_eX]

[DOI]

Stefano Sarao Mannelli

Lenka Zdeborová

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

A Dynamical Central Limit Theorem for Shallow Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Extreme Event Quantification in Dynamical Systems with Random Components.

[BibT_eX]

[DOI]

Giovanni Dematteis

Tobias Grafke

SIAM/ASA J. Uncertain. Quantification, 2019

Global convergence of neuron birth-death dynamics.

[BibT_eX]

[DOI]

CoRR, 2019

Neuron birth-death dynamics accelerates gradient descent and converges asymptotically.

[BibT_eX]

[DOI]

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Neural Networks as Interacting Particle Systems: Asymptotic Convexity of the Loss Landscape and Universal Scaling of the Approximation Error.

[BibT_eX]

[DOI]

CoRR, 2018

Parameters as interacting particles: long time convergence and asymptotic error scaling of neural networks.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

Metastability of the Nonlinear Wave Equation: Insights from Transition State Theory.

[BibT_eX]

[DOI]

Katherine Newhall

J. Nonlinear Sci., 2017

2016

Self-guided Langevin dynamics via generalized Langevin equation.

[BibT_eX]

[DOI]

Xiongwu Wu

Bernard R. Brooks

J. Comput. Chem., 2016

2014

Arclength Parametrized Hamilton's Equations for the Calculation of Instantons.

[BibT_eX]

[DOI]

Multiscale Model. Simul., 2014

Metropolis Integration Schemes for Self-Adjoint Diffusions.

[BibT_eX]

[DOI]

Nawaf Bou-Rabee

Aleksandar Donev

Multiscale Model. Simul., 2014

Flows in Complex Networks: Theory, Algorithms, and Application to Lennard-Jones Cluster Rearrangement.

[BibT_eX]

[DOI]

Maria Cameron

CoRR, 2014

2012

A patch that imparts unconditional stability to explicit integrators for Langevin-like equations.

[BibT_eX]

[DOI]

Nawaf Bou-Rabee

J. Comput. Phys., 2012

The heterogeneous multiscale method.

[BibT_eX]

[DOI]

Acta Numer., 2012

2011

Diffusion Estimation from Multiscale Data by Operator Eigenpairs.

[BibT_eX]

[DOI]

Daan Crommelin

Multiscale Model. Simul., 2011

The String Method as a Dynamical System.

[BibT_eX]

[DOI]

Maria Cameron

Robert V. Kohn

J. Nonlinear Sci., 2011

2010

Optimal Fuzzy Aggregation of Networks.

[BibT_eX]

[DOI]

Marco Sarich

Christof Schütte

Multiscale Model. Simul., 2010

Clustering and Classification through Normalizing Flows in Feature Space.

[BibT_eX]

[DOI]

Juan P. Agnelli

M. Cadeiras

Esteban G. Tabak

Cristina Vilma Turner

Multiscale Model. Simul., 2010

2009

Remarks on Drift Estimation for Diffusion Processes.

[BibT_eX]

[DOI]

Yvo Pokern

Andrew M. Stuart

Multiscale Model. Simul., 2009

Data-Based Inference of Generators for Markov Jump Processes Using Convex Optimization.

[BibT_eX]

[DOI]

Daan Crommelin

Multiscale Model. Simul., 2009

A general strategy for designing seamless multiscale methods.

[BibT_eX]

[DOI]

Weinan E

Weiqing Ren

J. Comput. Phys., 2009

Some recent techniques for free energy calculations.

[BibT_eX]

[DOI]

J. Comput. Chem., 2009

2008

Transition Path Theory for Markov Jump Processes.

[BibT_eX]

[DOI]

Philipp Metzner

Christof Schütte

Multiscale Model. Simul., 2008

Accelerated Simulation of a Heavy Particle in a Gas of Elastic Spheres.

[BibT_eX]

[DOI]

Gil Ariel

Multiscale Model. Simul., 2008

2007

Nested stochastic simulation algorithms for chemical kinetic systems with multiple time scales.

[BibT_eX]

[DOI]

Weinan E

Di Liu

J. Comput. Phys., 2007

2006

Fitting timeseries by continuous-time Markov chains: A quadratic programming approach.

[BibT_eX]

[DOI]

Daan Crommelin

J. Comput. Phys., 2006

2005

Magnetic Elements at Finite Temperature and Large Deviation Theory.

[BibT_eX]

[DOI]

Robert V. Kohn

Maria G. Reznikoff