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Enhancing Accuracy in Deep Learning Using Random Matrix Theory
We explore the applications of random matrix theory (RMT) in the training of deep neural networks (DNNs), focusing on layer pruning that is reducing the number of DNN parameters (weights).
A novel multi-scale loss function for classification problems in machine learning
We introduce two-scale loss functions for use in various gradient descent algorithms applied to classification problems via deep neural networks.
Stability for the Training of Deep Neural Networks and Other Classifiers
Our main result consists of two novel conditions on the classifier which, if either is satisfied, ensure stability of training, that is we derive tight bounds on accuracy as loss decreases.
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