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02/06/2022
Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data
The search for effective and robust generalization metrics has been the ...
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06/01/2021
Post-mortem on a deep learning contest: a Simpson's paradox and the complementary roles of scale metrics versus shape metrics
To understand better the causes of good generalization performance in st...
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02/17/2020
Predicting trends in the quality of state-of-the-art neural networks without access to training or testing data
In many applications, one works with deep neural network (DNN) models tr...
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01/24/2019
Heavy-Tailed Universality Predicts Trends in Test Accuracies for Very Large Pre-Trained Deep Neural Networks
Given two or more Deep Neural Networks (DNNs) with the same or similar a...
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01/24/2019
Traditional and Heavy-Tailed Self Regularization in Neural Network Models
Random Matrix Theory (RMT) is applied to analyze the weight matrices of ...
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10/02/2018
Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learning
Random Matrix Theory (RMT) is applied to analyze weight matrices of Deep...
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10/26/2017
