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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.
Predicting the Critical Number of Layers for Hierarchical Support Vector Regression
Hierarchical support vector regression (HSVR) models a function from data as a linear combination of SVR models at a range of scales, starting at a coarse scale and moving to finer scales as the hierarchy continues.
Applications of Koopman Mode Analysis to Neural Networks
We also show how using Koopman modes we can selectively prune the network to speed up the training procedure.
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