| 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 | 110 | 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 | 68 | 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 | 18 | 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 | 16 | 2022 |
| Variational Autoencoding Neural Operators JH Seidman, G Kissas, GJ Pappas, P Perdikaris International Conference on Machine Learning 202, 30491--30522, 2023 | 12 | 2023 |
| 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 | 9 | 2018 |
| Random Weight Factorization Improves the Training of Continuous Neural Representations S Wang, H Wang, JH Seidman, P Perdikaris arXiv preprint arXiv:2210.01274, 2022 | 8 | 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 | 8 | 2019 |
