Rethinking generalization requires revisiting old ideas: statistical mechanics approaches and complex learning behavior. - Charles H. Martin; Michael W. Mahoney (CoRR, 2017)

Traditional and Heavy Tailed Self Regularization in Neural Network Models. - Michael W. Mahoney; Charles H. Martin (ICML, 2019)

Statistical Mechanics Methods for Discovering Knowledge from Modern Production Quality Neural Networks. - Charles H. Martin; Michael W. Mahoney (KDD, 2019)

Heavy-Tailed Universality Predicts Trends in Test Accuracies for Very Large Pre-Trained Deep Neural Networks. - Charles H. Martin; Michael W. Mahoney (SDM, 2020)

Predicting trends in the quality of state-of-the-art neural networks without access to training or testing data. - Charles H. Martin; Tongsu Peng; Michael W. Mahoney (CoRR, 2020)

Post-mortem on a deep learning contest: a Simpson's paradox and the complementary roles of scale metrics versus shape metrics. - Charles H. Martin; Michael W. Mahoney (CoRR, 2021)

Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learning. - Charles H. Martin; Michael W. Mahoney (J. Mach. Learn. Res., 2021)

Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data. - Yaoqing Yang; Ryan Theisen; Liam Hodgkinson; Joseph E. Gonzalez; Kannan Ramchandran; Charles H. Martin; Michael W. Mahoney (CoRR, 2022)

Test Accuracy vs. Generalization Gap: Model Selection in NLP without Accessing Training or Testing Data. - Yaoqing Yang; Ryan Theisen; Liam Hodgkinson; Joseph E. Gonzalez; Kannan Ramchandran; Charles H. Martin; Michael W. Mahoney (KDD, 2023)

Temperature Balancing, Layer-wise Weight Analysis, and Neural Network Training. - Yefan Zhou; Tianyu Pang; Keqin Liu; Charles H. Martin; Michael W. Mahoney; Yaoqing Yang (NeurIPS, 2023)

The recent Physics and Chemistry Nobel Prizes, AI, and the convergence of knowledge fields. - Charles H. Martin; Ganesh Mani (Patterns, 2024)

Grokking and Generalization Collapse: Insights from HTSR theory. - Hari Prakash; Charles H. Martin (CoRR, 2025)

SETOL: A Semi-Empirical Theory of (Deep) Learning. - Charles H. Martin; Christopher Hinrichs (CoRR, 2025)