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Koopman Learning with Episodic Memory
We find that a basic implementation of Koopman learning with episodic memory leads to significant improvements in prediction on synthetic and real-world data.
On Equivalent Optimization of Machine Learning Methods
Our data-driven approach is general and can be utilized broadly to compare the optimization of machine learning methods.
An Operator Theoretic View on Pruning Deep Neural Networks
The discovery of sparse subnetworks that are able to perform as well as full models has found broad applied and theoretical interest.
Universality of Winning Tickets: A Renormalization Group Perspective
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.
