Speaker: Dr. Zerotti Woods, Johns Hopkins University
Abstract: In this work, we present a new regularization term that penalizes the conditioning of the weight matrices in a deep neural network. We give a mathematical argument that suggests that in certain situations, the conditioning number of the weight matrices have a direct impact on the error in classification. Empirical evidence suggests that improving the weight matrix associated with the output layer of a matrix improves generalizability when classifying ECG data from a benchmark data-set, and also a novel malaria infection data-set.
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Meeting ID: 939 5079 6547
Friday, April 16, 2021 at 2:00pm to 3:00pmVirtual Event
Academic Affairs, Mathematics (Department of), Fisher College of Science and Mathematics
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