On Regularization and Robustness of Deep Neural Networks. - Alberto Bietti; Grégoire Mialon; Julien Mairal (CoRR, 2018)

A Kernel Perspective for Regularizing Deep Neural Networks. - Alberto Bietti; Grégoire Mialon; Dexiong Chen; Julien Mairal (ICML, 2019)

Screening Data Points in Empirical Risk Minimization via Ellipsoidal Regions and Safe Loss Function. - Grégoire Mialon; Alexandre d'Aspremont; Julien Mairal (CoRR, 2019)

Screening Data Points in Empirical Risk Minimization via Ellipsoidal Regions and Safe Loss Functions. - Grégoire Mialon; Julien Mairal; Alexandre d'Aspremont (AISTATS, 2020)

An Optimal Transport Kernel for Feature Aggregation and its Relationship to Attention. - Grégoire Mialon; Dexiong Chen; Alexandre d'Aspremont; Julien Mairal (CoRR, 2020)

A Trainable Optimal Transport Embedding for Feature Aggregation and its Relationship to Attention. - Grégoire Mialon; Dexiong Chen; Alexandre d'Aspremont; Julien Mairal (ICLR, 2021)

GraphiT: Encoding Graph Structure in Transformers. - Grégoire Mialon; Dexiong Chen; Margot Selosse; Julien Mairal (CoRR, 2021)

Variance Covariance Regularization Enforces Pairwise Independence in Self-Supervised Representations. - Grégoire Mialon; Randall Balestriero; Yann LeCun (CoRR, 2022)

On Inductive Biases for Machine Learning in Data Constrained Settings. (Biais inductifs pour l'apprentissage automatique à partir de données limitées). - Grégoire Mialon (2022)

Self-Supervised Learning with Lie Symmetries for Partial Differential Equations. - Grégoire Mialon; Quentin Garrido; Hannah Lawrence; Danyal Rehman; Yann LeCun; Bobak T. Kiani (NeurIPS, 2023)

On Inductive Biases for Machine Learning in Data Constrained Settings. - Grégoire Mialon (CoRR, 2023)

A Cookbook of Self-Supervised Learning. - Randall Balestriero; Mark Ibrahim; Vlad Sobal; Ari Morcos; Shashank Shekhar; Tom Goldstein; Florian Bordes; Adrien Bardes; Grégoire Mialon; Yuandong Tian; Avi Schwarzschild; Andrew Gordon Wilson; et al. (CoRR, 2023)

GAIA: a benchmark for General AI Assistants. - Grégoire Mialon; Clémentine Fourrier; Craig Swift; Thomas Wolf 008; Yann LeCun; Thomas Scialom (CoRR, 2023)

WorldSense: A Synthetic Benchmark for Grounded Reasoning in Large Language Models. - Youssef Benchekroun; Megi Dervishi; Mark Ibrahim; Jean-Baptiste Gaya; Xavier Martinet; Grégoire Mialon; Thomas Scialom; Emmanuel Dupoux; Dieuwke Hupkes; Pascal Vincent (CoRR, 2023)

Augmented Language Models: a Survey. - Grégoire Mialon; Roberto Dessì; Maria Lomeli; Christoforos Nalmpantis; Ramakanth Pasunuru; Roberta Raileanu; Baptiste Rozière; Timo Schick; Jane Dwivedi-Yu; Asli Celikyilmaz; Edouard Grave; Yann LeCun; et al. (Trans. Mach. Learn. Res., 2023)

GAIA: a benchmark for General AI Assistants. - Grégoire Mialon; Clémentine Fourrier; Thomas Wolf 008; Yann LeCun; Thomas Scialom (ICLR, 2024)

The Llama 3 Herd of Models. - Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Ahmad Al-Dahle; Aiesha Letman; Akhil Mathur; Alan Schelten; Amy Yang; Angela Fan; Anirudh Goyal; Anthony Hartshorn; et al. (CoRR, 2024)