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ORCIDEric Vanden-Eijnden
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2020 – today
- 2024
[j22]Joan Bruna, Benjamin Peherstorfer, Eric Vanden-Eijnden:
Neural Galerkin schemes with active learning for high-dimensional evolution equations. J. Comput. Phys. 496: 112588 (2024)- [c16]Nanye Ma, Mark Goldstein, Michael S. Albergo, Nicholas M. Boffi, Eric Vanden-Eijnden, Saining Xie:
SiT: Exploring Flow and Diffusion-Based Generative Models with Scalable Interpolant Transformers. ECCV (77) 2024: 23-40 - [c15]Michael S. Albergo, Nicholas Matthew Boffi, Michael Lindsey, Eric Vanden-Eijnden:
Multimarginal Generative Modeling with Stochastic Interpolants. ICLR 2024 - [c14]Hugo Cui, Florent Krzakala, Eric Vanden-Eijnden, Lenka Zdeborová:
Analysis of Learning a Flow-based Generative Model from Limited Sample Complexity. ICLR 2024 - [c13]Michael S. Albergo, Mark Goldstein, Nicholas Matthew Boffi, Rajesh Ranganath, Eric Vanden-Eijnden:
Stochastic Interpolants with Data-Dependent Couplings. ICML 2024 - [c12]Yifan Chen, Mark Goldstein, Mengjian Hua, Michael S. Albergo, Nicholas Matthew Boffi, Eric Vanden-Eijnden:
Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes. ICML 2024 - [i29]Nanye Ma, Mark Goldstein, Michael S. Albergo, Nicholas M. Boffi, Eric Vanden-Eijnden, Saining Xie:
SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers. CoRR abs/2401.08740 (2024) - [i28]Yifan Chen, Mark Goldstein, Mengjian Hua, Michael S. Albergo, Nicholas M. Boffi, Eric Vanden-Eijnden:
Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes. CoRR abs/2403.13724 (2024) - [i27]Huan Zhang, Yifan Chen, Eric Vanden-Eijnden, Benjamin Peherstorfer:
Sequential-in-time training of nonlinear parametrizations for solving time-dependent partial differential equations. CoRR abs/2404.01145 (2024) - [i26]Nicholas M. Boffi, Michael S. Albergo, Eric Vanden-Eijnden:
Flow Map Matching. CoRR abs/2406.07507 (2024) - [i25]Michael S. Albergo, Eric Vanden-Eijnden:
NETS: A Non-Equilibrium Transport Sampler. CoRR abs/2410.02711 (2024) - 2023
- [j21]Nicholas M. Boffi
, Eric Vanden-Eijnden:
Probability flow solution of the Fokker-Planck equation. Mach. Learn. Sci. Technol. 4(3): 35012 (2023) - [c11]Michael S. Albergo, Eric Vanden-Eijnden:
Building Normalizing Flows with Stochastic Interpolants. ICLR 2023 - [c10]Davide Carbone, Mengjian Hua, Simon Coste, Eric Vanden-Eijnden:
Efficient Training of Energy-Based Models Using Jarzynski Equality. NeurIPS 2023 - [i24]Michael S. Albergo, Nicholas M. Boffi, Eric Vanden-Eijnden:
Stochastic Interpolants: A Unifying Framework for Flows and Diffusions. CoRR abs/2303.08797 (2023) - [i23]Davide Carbone, Mengjian Hua, Simon Coste, Eric Vanden-Eijnden:
Efficient Training of Energy-Based Models Using Jarzynski Equality. CoRR abs/2305.19414 (2023) - [i22]Yuxiao Wen, Eric Vanden-Eijnden, Benjamin Peherstorfer:
Coupling parameter and particle dynamics for adaptive sampling in Neural Galerkin schemes. CoRR abs/2306.15630 (2023) - [i21]Nicholas M. Boffi, Eric Vanden-Eijnden:
Deep learning probability flows and entropy production rates in active matter. CoRR abs/2309.12991 (2023) - [i20]Hugo Cui, Florent Krzakala, Eric Vanden-Eijnden, Lenka Zdeborová:
Analysis of learning a flow-based generative model from limited sample complexity. CoRR abs/2310.03575 (2023) - [i19]Michael S. Albergo, Nicholas M. Boffi, Michael Lindsey, Eric Vanden-Eijnden:
Multimarginal generative modeling with stochastic interpolants. CoRR abs/2310.03695 (2023) - [i18]Michael S. Albergo, Mark Goldstein, Nicholas M. Boffi, Rajesh Ranganath, Eric Vanden-Eijnden:
Stochastic interpolants with data-dependent couplings. CoRR abs/2310.03725 (2023) - [i17]Michael S. Albergo, Eric Vanden-Eijnden:
Learning to Sample Better. CoRR abs/2310.11232 (2023) - 2022
- [c9]Zhengdao Chen, Eric Vanden-Eijnden, Joan Bruna:
On feature learning in neural networks with global convergence guarantees. ICLR 2022 - [c8]Yu Cao, Eric Vanden-Eijnden:
Learning Optimal Flows for Non-Equilibrium Importance Sampling. NeurIPS 2022 - [c7]Leonardo Petrini, Francesco Cagnetta, Eric Vanden-Eijnden, Matthieu Wyart:
Learning sparse features can lead to overfitting in neural networks. NeurIPS 2022 - [i16]Joan Bruna, Benjamin Peherstorfer, Eric Vanden-Eijnden:
Neural Galerkin Scheme with Active Learning for High-Dimensional Evolution Equations. CoRR abs/2203.01360 (2022) - [i15]Zhengdao Chen, Eric Vanden-Eijnden, Joan Bruna:
On Feature Learning in Neural Networks with Global Convergence Guarantees. CoRR abs/2204.10782 (2022) - [i14]Nicholas M. Boffi, Eric Vanden-Eijnden:
Probability flow solution of the Fokker-Planck equation. CoRR abs/2206.04642 (2022) - [i13]Leonardo Petrini, Francesco Cagnetta, Eric Vanden-Eijnden, Matthieu Wyart:
Learning sparse features can lead to overfitting in neural networks. CoRR abs/2206.12314 (2022) - [i12]Michael S. Albergo, Eric Vanden-Eijnden:
Building Normalizing Flows with Stochastic Interpolants. CoRR abs/2209.15571 (2022) - [i11]Zhengdao Chen, Eric Vanden-Eijnden, Joan Bruna:
A Functional-Space Mean-Field Theory of Partially-Trained Three-Layer Neural Networks. CoRR abs/2210.16286 (2022) - 2021
- [c6]Carles Domingo-Enrich, Alberto Bietti, Eric Vanden-Eijnden, Joan Bruna:
On Energy-Based Models with Overparametrized Shallow Neural Networks. ICML 2021: 2771-2782 - [c5]Grant M. Rotskoff, Andrew R. Mitchell, Eric Vanden-Eijnden:
Active Importance Sampling for Variational Objectives Dominated by Rare Events: Consequences for Optimization and Generalization. MSML 2021: 757-780 - [i10]Carles Domingo-Enrich, Alberto Bietti, Eric Vanden-Eijnden, Joan Bruna:
On Energy-Based Models with Overparametrized Shallow Neural Networks. CoRR abs/2104.07531 (2021) - [i9]Carles Domingo-Enrich, Alberto Bietti, Marylou Gabrié, Joan Bruna, Eric Vanden-Eijnden:
Dual Training of Energy-Based Models with Overparametrized Shallow Neural Networks. CoRR abs/2107.05134 (2021) - [i8]Marylou Gabrié, Grant M. Rotskoff, Eric Vanden-Eijnden:
Efficient Bayesian Sampling Using Normalizing Flows to Assist Markov Chain Monte Carlo Methods. CoRR abs/2107.08001 (2021) - [i7]Shanyin Tong, Eric Vanden-Eijnden, Georg Stadler:
Estimating earthquake-induced tsunami inundation probabilities without sampling. CoRR abs/2111.14325 (2021) - 2020
- [c4]Zhengdao Chen, Grant M. Rotskoff, Joan Bruna, Eric Vanden-Eijnden:
A Dynamical Central Limit Theorem for Shallow Neural Networks. NeurIPS 2020 - [c3]Stefano Sarao Mannelli, Eric Vanden-Eijnden, Lenka Zdeborová:
Optimization and Generalization of Shallow Neural Networks with Quadratic Activation Functions. NeurIPS 2020 - [i6]Stefano Sarao Mannelli, Eric Vanden-Eijnden, Lenka Zdeborová:
Optimization and Generalization of Shallow Neural Networks with Quadratic Activation Functions. CoRR abs/2006.15459 (2020) - [i5]Shanyin Tong, Eric Vanden-Eijnden, Georg Stadler:
Extreme event probability estimation using PDE-constrained optimization and large deviation theory, with application to tsunamis. CoRR abs/2007.13930 (2020) - [i4]Zhengdao Chen, Grant M. Rotskoff, Joan Bruna, Eric Vanden-Eijnden:
A Dynamical Central Limit Theorem for Shallow Neural Networks. CoRR abs/2008.09623 (2020)
2010 – 2019
- 2019
- [j20]Giovanni Dematteis, Tobias Grafke, Eric Vanden-Eijnden:
Extreme Event Quantification in Dynamical Systems with Random Components. SIAM/ASA J. Uncertain. Quantification 7(3): 1029-1059 (2019) - [c2]Grant M. Rotskoff, Samy Jelassi, Joan Bruna, Eric Vanden-Eijnden:
Neuron birth-death dynamics accelerates gradient descent and converges asymptotically. ICML 2019: 5508-5517 - [i3]Grant M. Rotskoff, Samy Jelassi, Joan Bruna, Eric Vanden-Eijnden:
Global convergence of neuron birth-death dynamics. CoRR abs/1902.01843 (2019) - 2018
- [c1]Grant M. Rotskoff, Eric Vanden-Eijnden:
Parameters as interacting particles: long time convergence and asymptotic error scaling of neural networks. NeurIPS 2018: 7146-7155 - [i2]Grant M. Rotskoff, Eric Vanden-Eijnden:
Neural Networks as Interacting Particle Systems: Asymptotic Convexity of the Loss Landscape and Universal Scaling of the Approximation Error. CoRR abs/1805.00915 (2018) - 2017
- [j19]Katherine Newhall, Eric Vanden-Eijnden:
Metastability of the Nonlinear Wave Equation: Insights from Transition State Theory. J. Nonlinear Sci. 27(3): 1007-1042 (2017) - 2016
- [j18]Xiongwu Wu, Bernard R. Brooks, Eric Vanden-Eijnden:
Self-guided Langevin dynamics via generalized Langevin equation. J. Comput. Chem. 37(6): 595-601 (2016) - 2014
- [j17]Tobias Grafke, Rainer Grauer, T. Schäfer, Eric Vanden-Eijnden:
Arclength Parametrized Hamilton's Equations for the Calculation of Instantons. Multiscale Model. Simul. 12(2): 566-580 (2014) - [j16]Nawaf Bou-Rabee, Aleksandar Donev, Eric Vanden-Eijnden:
Metropolis Integration Schemes for Self-Adjoint Diffusions. Multiscale Model. Simul. 12(2): 781-831 (2014) - [i1]Maria Cameron, Eric Vanden-Eijnden:
Flows in Complex Networks: Theory, Algorithms, and Application to Lennard-Jones Cluster Rearrangement. CoRR abs/1402.1736 (2014) - 2012
- [j15]Assyr Abdulle, Weinan E, Björn Engquist, Eric Vanden-Eijnden:
The heterogeneous multiscale method. Acta Numer. 21: 1-87 (2012) - [j14]Nawaf Bou-Rabee, Eric Vanden-Eijnden:
A patch that imparts unconditional stability to explicit integrators for Langevin-like equations. J. Comput. Phys. 231(6): 2565-2580 (2012) - 2011
- [j13]Maria Cameron, Robert V. Kohn, Eric Vanden-Eijnden:
The String Method as a Dynamical System. J. Nonlinear Sci. 21(2): 193-230 (2011) - [j12]Daan Crommelin, Eric Vanden-Eijnden:
Diffusion Estimation from Multiscale Data by Operator Eigenpairs. Multiscale Model. Simul. 9(4): 1588-1623 (2011) - 2010
- [j11]Marco Sarich, Christof Schütte, Eric Vanden-Eijnden:
Optimal Fuzzy Aggregation of Networks. Multiscale Model. Simul. 8(4): 1535-1561 (2010) - [j10]Juan P. Agnelli, M. Cadeiras, Esteban G. Tabak, Cristina Vilma Turner, Eric Vanden-Eijnden:
Clustering and Classification through Normalizing Flows in Feature Space. Multiscale Model. Simul. 8(5): 1784-1802 (2010)
2000 – 2009
- 2009
- [j9]Eric Vanden-Eijnden:
Some recent techniques for free energy calculations. J. Comput. Chem. 30(11): 1737-1747 (2009) - [j8]Weinan E, Weiqing Ren, Eric Vanden-Eijnden:
A general strategy for designing seamless multiscale methods. J. Comput. Phys. 228(15): 5437-5453 (2009) - [j7]Daan Crommelin, Eric Vanden-Eijnden:
Data-Based Inference of Generators for Markov Jump Processes Using Convex Optimization. Multiscale Model. Simul. 7(4): 1751-1778 (2009) - [j6]Yvo Pokern, Andrew M. Stuart, Eric Vanden-Eijnden:
Remarks on Drift Estimation for Diffusion Processes. Multiscale Model. Simul. 8(1): 69-95 (2009) - 2008
- [j5]Gil Ariel, Eric Vanden-Eijnden:
Accelerated Simulation of a Heavy Particle in a Gas of Elastic Spheres. Multiscale Model. Simul. 7(1): 349-361 (2008) - [j4]Philipp Metzner, Christof Schütte, Eric Vanden-Eijnden:
Transition Path Theory for Markov Jump Processes. Multiscale Model. Simul. 7(3): 1192-1219 (2008) - 2007
- [j3]Weinan E, Di Liu, Eric Vanden-Eijnden:
Nested stochastic simulation algorithms for chemical kinetic systems with multiple time scales. J. Comput. Phys. 221(1): 158-180 (2007) - 2006
- [j2]Daan Crommelin, Eric Vanden-Eijnden:
Fitting timeseries by continuous-time Markov chains: A quadratic programming approach. J. Comput. Phys. 217(2): 782-805 (2006) - 2005
- [j1]Robert V. Kohn, Maria G. Reznikoff, Eric Vanden-Eijnden:
Magnetic Elements at Finite Temperature and Large Deviation Theory. J. Nonlinear Sci. 15(4): 223-253 (2005)
Coauthor Index
aka: Nicholas Matthew Boffi
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