Charles H. Martin | DeepAI

DeepAI AI Chat

Featured Co-authors

Michael W. Mahoney
126 publications
Joseph E. Gonzalez
99 publications
Kannan Ramchandran
53 publications
Yaoqing Yang
20 publications
Liam Hodgkinson
13 publications
Ryan Theisen
6 publications
Tongsu
1 publication

∙ 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 ...

3 Yaoqing Yang, et al. ∙

∙ 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...

8 Charles H. Martin, et al. ∙

∙ 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...

0 Charles H. Martin, et al. ∙

∙ 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...

4 Charles H. Martin, et al. ∙

∙ 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 ...

0 Charles H. Martin, et al. ∙

∙ 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...

0 Charles H. Martin, et al. ∙

∙ 10/26/2017

Rethinking generalization requires revisiting old ideas: statistical mechanics approaches and complex learning behavior

We describe an approach to understand the peculiar and counterintuitive ...

0 Charles H. Martin, et al. ∙

Are you a researcher?
Expose your work
to one of the largest
A.I. communities in the world