Explore a 26-minute conference talk from the Fourth Symposium on Machine Learning and Dynamical Systems, presented by Yuka Hashimoto of NTT Network Service Systems Laboratories. Delve into the concept of Koopman-based generalization bounds for neural networks, gaining insights into this advanced topic in machine learning and dynamical systems. Discover how this approach contributes to understanding the performance and limitations of neural network models.
Koopman-Based Generalization Bound for Neural Networks
Overview
Syllabus
Koopman-based generalization bound for neural networks
Taught by
Fields Institute
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