Two co-authored papers have been accepted for publication in ICLR 2021
- On the Curse of Memory in Recurrent Neural Networks: Approximation and Optimization Analysis: In this paper, we formulate the basic mathematical setting of learning dynamical relationships using recurrent neural networks, and uncover precisely the effect of memory on approximation and optimization in the linear setting.
- Towards Robust Neural Networks via Close-loop Control: In this paper, we use the optimal control formulation of deep learning to develop adversarial defense mechanisms based on closed-loop control.
A co-authored paper has been accepted at AAAI 2021
- Amata: An Annealing Mechanism for Adversarial Training Acceleration: In this paper, we employ control-theoretic approaches to analyze and improve upon classical PGD based methods for adversarial training.