Grégoire Mialon

According to our database, Grégoire Mialon authored at least 16 papers between 2018 and 2023.

Collaborative distances:

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

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

GAIA: a benchmark for General AI Assistants.

Grégoire Mialon, Clémentine Fourrier, Craig Swift, Thomas Wolf, Yann LeCun, Thomas Scialom

CoRR, 2023

Self-Supervised Learning with Lie Symmetries for Partial Differential Equations.

Grégoire Mialon, Quentin Garrido, Hannah Lawrence, Danyal Rehman, Yann LeCun, Bobak Toussi Kiani

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, Jonas Geiping, Quentin Garrido, Pierre Fernandez, Amir Bar, Hamed Pirsiavash, Yann LeCun, Micah Goldblum

CoRR, 2023

On Inductive Biases for Machine Learning in Data Constrained Settings.

Grégoire Mialon

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, Thomas Scialom

CoRR, 2023

Self-Supervised Learning with Lie Symmetries for Partial Differential Equations.

Grégoire Mialon, Quentin Garrido, Hannah Lawrence, Danyal Rehman, Yann LeCun, Bobak T. Kiani

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

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

Grégoire Mialon

PhD thesis, 2022

Variance Covariance Regularization Enforces Pairwise Independence in Self-Supervised Representations.

Grégoire Mialon, Randall Balestriero, Yann LeCun

CoRR, 2022

2021

GraphiT: Encoding Graph Structure in Transformers.

Grégoire Mialon, Dexiong Chen, Margot Selosse, Julien Mairal

CoRR, 2021

A Trainable Optimal Transport Embedding for Feature Aggregation and its Relationship to Attention.

Grégoire Mialon, Dexiong Chen, Alexandre d'Aspremont, Julien Mairal

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

An Optimal Transport Kernel for Feature Aggregation and its Relationship to Attention.

Grégoire Mialon, Dexiong Chen, Alexandre d'Aspremont, Julien Mairal

CoRR, 2020

Screening Data Points in Empirical Risk Minimization via Ellipsoidal Regions and Safe Loss Functions.

Grégoire Mialon, Julien Mairal, Alexandre d'Aspremont

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Screening Data Points in Empirical Risk Minimization via Ellipsoidal Regions and Safe Loss Function.

Grégoire Mialon, Alexandre d'Aspremont, Julien Mairal

CoRR, 2019

A Kernel Perspective for Regularizing Deep Neural Networks.

Alberto Bietti, Grégoire Mialon, Dexiong Chen, Julien Mairal

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

On Regularization and Robustness of Deep Neural Networks.

Alberto Bietti, Grégoire Mialon, Julien Mairal

CoRR, 2018