2 papers
cs.LG2024
Novel Deep Neural Network Classifier Characterization Metrics with Applications to Dataless Evaluation
Nathaniel Dean, Dilip Sarkar
The mainstream AI community has seen a rise in large-scale open-source classifiers, often pre-trained on vast datasets and tested on standard benchmarks; however, users facing dive…
cs.LG2024
Minimizing Chebyshev Prototype Risk Magically Mitigates the Perils of Overfitting
Nathaniel Dean, Dilip Sarkar
Overparameterized deep neural networks (DNNs), if not sufficiently regularized, are susceptible to overfitting their training examples and not generalizing well to test data. To di…