Momentum Residual Neural Networks. - Michael E. Sander; Pierre Ablin; Mathieu Blondel; Gabriel Peyré (ICML, 2021)

Sinkformers: Transformers with Doubly Stochastic Attention. - Michael E. Sander; Pierre Ablin; Mathieu Blondel; Gabriel Peyré (AISTATS, 2022)

Vision Transformers provably learn spatial structure. - Samy Jelassi; Michael E. Sander; Yuanzhi Li (NeurIPS, 2022)

Do Residual Neural Networks discretize Neural Ordinary Differential Equations? - Michael E. Sander; Pierre Ablin; Gabriel Peyré (NeurIPS, 2022)

Fast, Differentiable and Sparse Top-k: a Convex Analysis Perspective. - Michael Eli Sander; Joan Puigcerver; Josip Djolonga; Gabriel Peyré; Mathieu Blondel (ICML, 2023)

Implicit regularization of deep residual networks towards neural ODEs. - Pierre Marion; Yu-Han Wu; Michael Eli Sander; Gérard Biau (ICLR, 2024)

How do Transformers Perform In-Context Autoregressive Learning ? - Michael Eli Sander; Raja Giryes; Taiji Suzuki; Mathieu Blondel; Gabriel Peyré (ICML, 2024)

Towards Understanding the Universality of Transformers for Next-Token Prediction. - Michael Eli Sander; Gabriel Peyré (ICLR, 2025)

Joint Learning of Energy-based Models and their Partition Function. - Michael E. Sander; Vincent Roulet; Tianlin Liu; Mathieu Blondel (CoRR, 2025)

Loss Functions and Operators Generated by f-Divergences. - Vincent Roulet; Tianlin Liu; Nino Vieillard; Michael E. Sander; Mathieu Blondel (CoRR, 2025)