COT-GAN: Generating Sequential Data via Causal Optimal Transport. - Tianlin Xu; Li Kevin Wenliang; Michael Munn; Beatrice Acciaio (NeurIPS, 2020)

The Geometric Occam's Razor Implicit in Deep Learning. - Benoit Dherin; Michael Munn; David G. T. Barrett (CoRR, 2021)

Why neural networks find simple solutions: The many regularizers of geometric complexity. - Benoit Dherin; Michael Munn; Mihaela Rosca; David Barrett 001 (NeurIPS, 2022)

A margin-based multiclass generalization bound via geometric complexity. - Michael Munn; Benoit Dherin; Javier Gonzalvo (TAG-ML, 2023)

Unified Functional Hashing in Automatic Machine Learning. - Ryan Gillard; Stephen Jonany; Yingjie Miao; Michael Munn; Connal de Souza; Jonathan Dungay; Chen Liang; David R. So; Quoc V. Le; Esteban Real (CoRR, 2023)

The Impact of Geometric Complexity on Neural Collapse in Transfer Learning. - Michael Munn; Benoit Dherin; Javier Gonzalvo (NeurIPS, 2024)

Leveraging free energy in pretraining model selection for improved fine-tuning. - Michael Munn; Susan Wei (CoRR, 2024)

Training in reverse: How iteration order influences convergence and stability in deep learning. - Benoit Dherin; Benny Avelin; Anders Karlsson; Hanna Mazzawi; Javier Gonzalvo; Michael Munn (CoRR, 2025)

Learning by solving differential equations. - Benoit Dherin; Michael Munn; Hanna Mazzawi; Michael Wunder; Sourabh Medapati; Javier Gonzalvo (CoRR, 2025)

Learning without training: The implicit dynamics of in-context learning. - Benoit Dherin; Michael Munn; Hanna Mazzawi; Michael Wunder; Javier Gonzalvo (CoRR, 2025)