no code implementations • 7 Oct 2021 • William T. Redman, Tianlong Chen, Zhangyang Wang, Akshunna S. Dogra
Foundational work on the Lottery Ticket Hypothesis has suggested an exciting corollary: winning tickets found in the context of one task can be transferred to similar tasks, possibly even across different architectures.
no code implementations • 29 Sep 2021 • William T Redman, Tianlong Chen, Akshunna S. Dogra, Zhangyang Wang
Foundational work on the Lottery Ticket Hypothesis has suggested an exciting corollary: winning tickets found in the context of one task can be transferred to similar tasks, possibly even across different architectures.
no code implementations • 24 Aug 2020 • Akshunna S. Dogra, William T Redman
Neural networks have been identified as powerful tools for the study of complex systems.
no code implementations • 9 Jul 2020 • Akshunna S. Dogra
Our methods do not require advance knowledge of the true solutions and obtain explicit relationships between loss functions and the error associated with solution estimates.
no code implementations • NeurIPS 2020 • Akshunna S. Dogra, William T Redman
Koopman operator theory, a powerful framework for discovering the underlying dynamics of nonlinear dynamical systems, was recently shown to be intimately connected with neural network training.
no code implementations • 20 Apr 2020 • Akshunna S. Dogra
Neural Networks (NNs) have been identified as a potentially powerful tool in the study of complex dynamical systems.
Dynamical Systems Signal Processing
1 code implementation • 29 Jan 2020 • Marios Mattheakis, David Sondak, Akshunna S. Dogra, Pavlos Protopapas
There has been a wave of interest in applying machine learning to study dynamical systems.