56 citations · 63 across the 4 of their papers we have counts for
5 papers
Random Weight Factorization Improves the Training of Continuous Neural Representations
Sifan Wang, Hanwen Wang, Jacob H. Seidman +1
Continuous neural representations have recently emerged as a powerful and flexible alternative to classical discretized representations of signals. However, training them to captur…
Learning Operators with Coupled Attention
Georgios Kissas, Jacob Seidman, Leonardo Ferreira Guilhoto +3
Supervised operator learning is an emerging machine learning paradigm with applications to modeling the evolution of spatio-temporal dynamical systems and approximating general bla…
Robust Deep Learning as Optimal Control: Insights and Convergence Guarantees
Jacob H. Seidman, Mahyar Fazlyab, Victor M. Preciado +1
The fragility of deep neural networks to adversarially-chosen inputs has motivated the need to revisit deep learning algorithms. Including adversarial examples during training is a…
A Control-Theoretic Approach to Analysis and Parameter Selection of Douglas-Rachford Splitting
Jacob H. Seidman, Mahyar Fazlyab, Victor M. Preciado +1
Douglas-Rachford splitting and its equivalent dual formulation ADMM are widely used iterative methods in composite optimization problems arising in control and machine learning app…
A Chebyshev-Accelerated Primal-Dual Method for Distributed Optimization
Jacob H. Seidman, Mahyar Fazlyab, George J. Pappas +1
We consider a distributed optimization problem over a network of agents aiming to minimize a global objective function that is the sum of local convex and composite cost functions.…