Eric Vanden-Eijnden
According to our database1,
Eric Vanden-Eijnden
authored at least 53 papers
between 2005 and 2024.
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Bibliography
2024
Neural Galerkin schemes with active learning for high-dimensional evolution equations.
J. Comput. Phys., January, 2024
Sequential-in-time training of nonlinear parametrizations for solving time-dependent partial differential equations.
CoRR, 2024
CoRR, 2024
SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers.
CoRR, 2024
Proceedings of the Twelfth International Conference on Learning Representations, 2024
Proceedings of the Twelfth International Conference on Learning Representations, 2024
2023
Mach. Learn. Sci. Technol., September, 2023
CoRR, 2023
Coupling parameter and particle dynamics for adaptive sampling in Neural Galerkin schemes.
CoRR, 2023
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
Proceedings of the Eleventh International Conference on Learning Representations, 2023
2022
A Functional-Space Mean-Field Theory of Partially-Trained Three-Layer Neural Networks.
CoRR, 2022
Neural Galerkin Scheme with Active Learning for High-Dimensional Evolution Equations.
CoRR, 2022
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022
Proceedings of the Tenth International Conference on Learning Representations, 2022
2021
CoRR, 2021
Efficient Bayesian Sampling Using Normalizing Flows to Assist Markov Chain Monte Carlo Methods.
CoRR, 2021
CoRR, 2021
Active Importance Sampling for Variational Objectives Dominated by Rare Events: Consequences for Optimization and Generalization.
Proceedings of the Mathematical and Scientific Machine Learning, 2021
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.
CoRR, 2020
Optimization and Generalization of Shallow Neural Networks with Quadratic Activation Functions.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020
2019
SIAM/ASA J. Uncertain. Quantification, 2019
Neuron birth-death dynamics accelerates gradient descent and converges asymptotically.
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.
CoRR, 2018
Parameters as interacting particles: long time convergence and asymptotic error scaling of neural networks.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018
2017
J. Nonlinear Sci., 2017
2016
J. Comput. Chem., 2016
2014
Multiscale Model. Simul., 2014
Multiscale Model. Simul., 2014
Flows in Complex Networks: Theory, Algorithms, and Application to Lennard-Jones Cluster Rearrangement.
CoRR, 2014
2012
A patch that imparts unconditional stability to explicit integrators for Langevin-like equations.
J. Comput. Phys., 2012
2011
Multiscale Model. Simul., 2011
2010
Multiscale Model. Simul., 2010
2009
Data-Based Inference of Generators for Markov Jump Processes Using Convex Optimization.
Multiscale Model. Simul., 2009
2008
Multiscale Model. Simul., 2008
2007
Nested stochastic simulation algorithms for chemical kinetic systems with multiple time scales.
J. Comput. Phys., 2007
2006
Fitting timeseries by continuous-time Markov chains: A quadratic programming approach.
J. Comput. Phys., 2006
2005
J. Nonlinear Sci., 2005