Implicit Gradient Regularization. - David G. T. Barrett; Benoit Dherin (ICLR, 2021)

On the Origin of Implicit Regularization in Stochastic Gradient Descent. - Samuel L. Smith; Benoit Dherin; David G. T. Barrett; Soham De (ICLR, 2021)

Discretization Drift in Two-Player Games. - Mihaela Rosca; Yan Wu 010; Benoit Dherin; David Barrett 001 (ICML, 2021)

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)

Morse Neural Networks for Uncertainty Quantification. - Benoit Dherin; Huiyi Hu; Jie Ren 006; Michael W. Dusenberry; Balaji Lakshminarayanan (CoRR, 2023)

Implicit biases in multitask and continual learning from a backward error analysis perspective. - Benoit Dherin (CoRR, 2023)

On a continuous time model of gradient descent dynamics and instability in deep learning. - Mihaela Rosca; Yan Wu 010; Chongli Qin; Benoit Dherin (Trans. Mach. Learn. Res., 2023)

Deep Fusion: Efficient Network Training via Pre-trained Initializations. - Hanna Mazzawi; Javier Gonzalvo; Michael Wunder; Sammy Jerome; Benoit Dherin (ICML, 2024)

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

Corridor Geometry in Gradient-Based Optimization. - Benoit Dherin; Mihaela Rosca (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)