Jacob H Seidman
Jacob H Seidman
Postdoctoral Researcher, University of Pennsylvania
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Learning operators with coupled attention
G Kissas, JH Seidman, LF Guilhoto, VM Preciado, GJ Pappas, ...
Journal of Machine Learning Research 23 (215), 1-63, 2022
Nomad: Nonlinear manifold decoders for operator learning
J Seidman, G Kissas, P Perdikaris, GJ Pappas
Advances in Neural Information Processing Systems 35, 5601-5613, 2022
Robust deep learning as optimal control: Insights and convergence guarantees
JH Seidman, M Fazlyab, VM Preciado, GJ Pappas
Learning for Dynamics and Control, 884-893, 2020
Gaussian Process Port-Hamiltonian Systems: Bayesian Learning with Physics Prior
T Beckers, J Seidman, P Perdikaris, GJ Pappas
2022 IEEE 61st Conference on Decision and Control (CDC), 1447-1453, 2022
A Control-Theoretic Approach to Analysis and Parameter Selection of Douglas–Rachford Splitting
JH Seidman, M Fazlyab, VM Preciado, GJ Pappas
IEEE Control Systems Letters 4 (1), 199-204, 2019
A Chebyshev-Accelerated Primal-Dual Method for Distributed Optimization
JH Seidman, M Fazlyab, GJ Pappas, VM Preciado
2018 IEEE Conference on Decision and Control (CDC), 1775-1781, 2018
Variational Autoencoding Neural Operators
JH Seidman, G Kissas, GJ Pappas, P Perdikaris
International Conference on Machine Learning 202, 30491--30522, 2023
Random Weight Factorization Improves the Training of Continuous Neural Representations
S Wang, H Wang, JH Seidman, P Perdikaris
arXiv preprint arXiv:2210.01274, 2022
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