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Conference and Workshop Papers
- 2025
[c8]Michael Eli Sander, Gabriel Peyré:
Towards Understanding the Universality of Transformers for Next-Token Prediction. ICLR 2025- 2024
- [c7]Pierre Marion, Yu-Han Wu, Michael Eli Sander, Gérard Biau:
Implicit regularization of deep residual networks towards neural ODEs. ICLR 2024 - [c6]Michael Eli Sander, Raja Giryes, Taiji Suzuki, Mathieu Blondel, Gabriel Peyré:
How do Transformers Perform In-Context Autoregressive Learning ? ICML 2024 - 2023
- [c5]Michael Eli Sander, Joan Puigcerver, Josip Djolonga, Gabriel Peyré, Mathieu Blondel:
Fast, Differentiable and Sparse Top-k: a Convex Analysis Perspective. ICML 2023: 29919-29936 - 2022
- [c4]Michael E. Sander, Pierre Ablin, Mathieu Blondel, Gabriel Peyré:
Sinkformers: Transformers with Doubly Stochastic Attention. AISTATS 2022: 3515-3530 - [c3]Samy Jelassi, Michael E. Sander, Yuanzhi Li:
Vision Transformers provably learn spatial structure. NeurIPS 2022 - [c2]Michael E. Sander, Pierre Ablin, Gabriel Peyré:
Do Residual Neural Networks discretize Neural Ordinary Differential Equations? NeurIPS 2022 - 2021
- [c1]Michael E. Sander, Pierre Ablin, Mathieu Blondel, Gabriel Peyré:
Momentum Residual Neural Networks. ICML 2021: 9276-9287
Informal and Other Publications
- 2025
- [i10]Michael E. Sander, Vincent Roulet, Tianlin Liu, Mathieu Blondel:
Joint Learning of Energy-based Models and their Partition Function. CoRR abs/2501.18528 (2025) - [i9]Vincent Roulet, Tianlin Liu, Nino Vieillard, Michael E. Sander, Mathieu Blondel:
Loss Functions and Operators Generated by f-Divergences. CoRR abs/2501.18537 (2025) - 2024
- [i8]Michael E. Sander, Raja Giryes, Taiji Suzuki, Mathieu Blondel, Gabriel Peyré:
How do Transformers perform In-Context Autoregressive Learning? CoRR abs/2402.05787 (2024) - [i7]Michael E. Sander, Gabriel Peyré:
Towards Understanding the Universality of Transformers for Next-Token Prediction. CoRR abs/2410.03011 (2024) - 2023
- [i6]Michael E. Sander, Joan Puigcerver, Josip Djolonga, Gabriel Peyré, Mathieu Blondel:
Fast, Differentiable and Sparse Top-k: a Convex Analysis Perspective. CoRR abs/2302.01425 (2023) - [i5]Pierre Marion, Yu-Han Wu, Michael E. Sander, Gérard Biau:
Implicit regularization of deep residual networks towards neural ODEs. CoRR abs/2309.01213 (2023) - 2022
- [i4]Michael E. Sander, Pierre Ablin, Gabriel Peyré:
Do Residual Neural Networks discretize Neural Ordinary Differential Equations? CoRR abs/2205.14612 (2022) - [i3]Samy Jelassi, Michael E. Sander, Yuanzhi Li:
Vision Transformers provably learn spatial structure. CoRR abs/2210.09221 (2022) - 2021
- [i2]Michael E. Sander, Pierre Ablin, Mathieu Blondel, Gabriel Peyré:
Momentum Residual Neural Networks. CoRR abs/2102.07870 (2021) - [i1]Michael E. Sander, Pierre Ablin, Mathieu Blondel, Gabriel Peyré:
Sinkformers: Transformers with Doubly Stochastic Attention. CoRR abs/2110.11773 (2021)
Coauthor Index
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