Michael E. Sander

According to our database, Michael E. Sander authored at least 13 papers between 2021 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Clustering in Deep Stochastic Transformers.

Lev Fedorov, Michael E. Sander, Romuald Elie, Pierre Marion, Mathieu Laurière

CoRR, January, 2026

Differentiable Knapsack and Top-k Operators via Dynamic Programming.

Germain Vivier-Ardisson, Michael Eli Sander, Axel Parmentier, Mathieu Blondel

CoRR, January, 2026

2025

Autoregressive Language Models are Secretly Energy-Based Models: Insights into the Lookahead Capabilities of Next-Token Prediction.

Mathieu Blondel, Michael E. Sander, Germain Vivier-Ardisson, Tianlin Liu, Vincent Roulet

CoRR, December, 2025

Joint Learning of Energy-based Models and their Partition Function.

Michael Eli Sander, Vincent Roulet, Tianlin Liu, Mathieu Blondel

Proceedings of the Forty-second International Conference on Machine Learning, 2025

Loss Functions and Operators Generated by f-Divergences.

Vincent Roulet, Tianlin Liu, Nino Vieillard, Michael Eli Sander, Mathieu Blondel

Proceedings of the Forty-second International Conference on Machine Learning, 2025

Towards Understanding the Universality of Transformers for Next-Token Prediction.

Michael Eli Sander, Gabriel Peyré

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

How do Transformers Perform In-Context Autoregressive Learning ?

Michael Eli Sander, Raja Giryes, Taiji Suzuki, Mathieu Blondel, Gabriel Peyré

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Implicit regularization of deep residual networks towards neural ODEs.

Pierre Marion, Yu-Han Wu, Michael Eli Sander, Gérard Biau

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Fast, Differentiable and Sparse Top-k: a Convex Analysis Perspective.

Michael Eli Sander, Joan Puigcerver, Josip Djolonga, Gabriel Peyré, Mathieu Blondel

Proceedings of the International Conference on Machine Learning, 2023

2022

Do Residual Neural Networks discretize Neural Ordinary Differential Equations?

Michael E. Sander, Pierre Ablin, Gabriel Peyré

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Vision Transformers provably learn spatial structure.

Samy Jelassi, Michael E. Sander, Yuanzhi Li

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Sinkformers: Transformers with Doubly Stochastic Attention.

Michael E. Sander, Pierre Ablin, Mathieu Blondel, Gabriel Peyré

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Momentum Residual Neural Networks.

Michael E. Sander, Pierre Ablin, Mathieu Blondel, Gabriel Peyré

Proceedings of the 38th International Conference on Machine Learning, 2021