Jacob H. Seidman

According to our database, Jacob H. Seidman authored at least 8 papers between 2018 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Variational Autoencoding Neural Operators.

Jacob H. Seidman, Georgios Kissas, George J. Pappas, Paris Perdikaris

Proceedings of the International Conference on Machine Learning, 2023

2022

Learning Operators with Coupled Attention.

Georgios Kissas, Jacob H. Seidman, Leonardo Ferreira Guilhoto, Victor M. Preciado, George J. Pappas, Paris Perdikaris

J. Mach. Learn. Res., 2022

Random Weight Factorization Improves the Training of Continuous Neural Representations.

Sifan Wang, Hanwen Wang, Jacob H. Seidman, Paris Perdikaris

CoRR, 2022

NOMAD: Nonlinear Manifold Decoders for Operator Learning.

Jacob H. Seidman, Georgios Kissas, Paris Perdikaris, George J. Pappas

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

Gaussian Process Port-Hamiltonian Systems: Bayesian Learning with Physics Prior.

Thomas Beckers, Jacob H. Seidman, Paris Perdikaris, George J. Pappas

Proceedings of the 61st IEEE Conference on Decision and Control, 2022

2020

A Control-Theoretic Approach to Analysis and Parameter Selection of Douglas-Rachford Splitting.

Jacob H. Seidman, Mahyar Fazlyab, Victor M. Preciado, George J. Pappas

IEEE Control. Syst. Lett., 2020

Robust Deep Learning as Optimal Control: Insights and Convergence Guarantees.

Jacob H. Seidman, Mahyar Fazlyab, Victor M. Preciado, George J. Pappas

Proceedings of the 2nd Annual Conference on Learning for Dynamics and Control, 2020

2018

A Chebyshev-Accelerated Primal-Dual Method for Distributed Optimization.

Jacob H. Seidman, Mahyar Fazlyab, George J. Pappas, Victor M. Preciado

Proceedings of the 57th IEEE Conference on Decision and Control, 2018