KOOPMAN OPERATOR THEORETIC METHODS FOR EFFICIENT TRAINING AND ANALYSIS OF DEEP NEURAL NETWORKS
Abstract
Machine learning, particularly deep neural networks (DNNs), have become a ubiquitous tool for researchers across many scientific disciplines. The past decade has seen a tremendous increase in the capability of DNNs, as models have become increasingly large and intricate. However, such improvements have come at the cost of requiring significant computational resources and time, as training these DNNs is highly complicated. The very nature of their complexity makes standard analytical tools impractical for the design and analysis of DNNs. This proposal leverages Koopman operator theory, a data-driven and mathematically rich dynamical systems framework, to develop novel approaches for understanding the behavior of DNNs during training, and to make that training and later deployment efficient.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Apr 20, 2023
- Source ID
- FA95502210531
Entities
People
- Maria Fonoberova
Organizations
- Air Force Office of Scientific Research
- United States Air Force