Aristide Baratin

According to our database, Aristide Baratin authored at least 23 papers between 2018 and 2024.

Collaborative distances:

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

2024

Promoting Exploration in Memory-Augmented Adam using Critical Momenta.

Pranshu Malviya, Gonçalo Mordido, Aristide Baratin, Reza Babanezhad Harikandeh, Jerry Huang, Simon Lacoste-Julien, Razvan Pascanu, Sarath Chandar

Trans. Mach. Learn. Res., 2024

Torque-Aware Momentum.

Pranshu Malviya, Gonçalo Mordido, Aristide Baratin, Reza Babanezhad Harikandeh, Gintare Karolina Dziugaite, Razvan Pascanu, Sarath Chandar

CoRR, 2024

Any-Property-Conditional Molecule Generation with Self-Criticism using Spanning Trees.

Alexia Jolicoeur-Martineau, Aristide Baratin, Kisoo Kwon, Boris Knyazev, Yan Zhang

CoRR, 2024

Bias in Motion: Theoretical Insights into the Dynamics of Bias in SGD Training.

Anchit Jain, Rozhin Nobahari, Aristide Baratin, Stefano Sarao Mannelli

CoRR, 2024

Maxwell's Demon at Work: Efficient Pruning by Leveraging Saturation of Neurons.

Simon Dufort-Labbé, Pierluca D'Oro, Evgenii Nikishin, Razvan Pascanu, Pierre-Luc Bacon, Aristide Baratin

CoRR, 2024

Lookbehind-SAM: k steps back, 1 step forward.

Gonçalo Mordido, Pranshu Malviya, Aristide Baratin, Sarath Chandar

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

Unsupervised Concept Discovery Mitigates Spurious Correlations.

Md Rifat Arefin, Yan Zhang, Aristide Baratin, Francesco Locatello, Irina Rish, Dianbo Liu, Kenji Kawaguchi

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

How connectivity structure shapes rich and lazy learning in neural circuits.

Yuhan Helena Liu, Aristide Baratin, Jonathan Cornford, Stefan Mihalas, Eric Shea-Brown, Guillaume Lajoie

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Using Representation Expressiveness and Learnability to Evaluate Self-Supervised Learning Methods.

Yuchen Lu, Zhen Liu, Aristide Baratin, Romain Laroche, Aaron C. Courville, Alessandro Sordoni

Trans. Mach. Learn. Res., 2023

Lookbehind Optimizer: k steps back, 1 step forward.

Gonçalo Mordido, Pranshu Malviya, Aristide Baratin, Sarath Chandar

CoRR, 2023

CrossSplit: Mitigating Label Noise Memorization through Data Splitting.

Jihye Kim, Aristide Baratin, Yan Zhang, Simon Lacoste-Julien

Proceedings of the International Conference on Machine Learning, 2023

2022

Lazy vs hasty: linearization in deep networks impacts learning schedule based on example difficulty.

Thomas George, Guillaume Lajoie, Aristide Baratin

Trans. Mach. Learn. Res., 2022

Expressiveness and Learnability: A Unifying View for Evaluating Self-Supervised Learning.

Yuchen Lu, Zhen Liu, Aristide Baratin, Romain Laroche, Aaron C. Courville, Alessandro Sordoni

CoRR, 2022

2021

On the Regularity of Attention.

James Vuckovic, Aristide Baratin, Remi Tachet des Combes

CoRR, 2021

Implicit Regularization via Neural Feature Alignment.

Aristide Baratin, Thomas George, César Laurent, R. Devon Hjelm, Guillaume Lajoie, Pascal Vincent, Simon Lacoste-Julien

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Implicit Regularization in Deep Learning: A View from Function Space.

Aristide Baratin, Thomas George, César Laurent, R. Devon Hjelm, Guillaume Lajoie, Pascal Vincent, Simon Lacoste-Julien

CoRR, 2020

A Mathematical Theory of Attention.

James Vuckovic, Aristide Baratin, Remi Tachet des Combes

CoRR, 2020

2019

On the Spectral Bias of Neural Networks.

Nasim Rahaman, Aristide Baratin, Devansh Arpit, Felix Draxler, Min Lin, Fred A. Hamprecht, Yoshua Bengio, Aaron C. Courville

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

2018

A Modern Take on the Bias-Variance Tradeoff in Neural Networks.

Brady Neal, Sarthak Mittal, Aristide Baratin, Vinayak Tantia, Matthew Scicluna, Simon Lacoste-Julien, Ioannis Mitliagkas

CoRR, 2018

On the Spectral Bias of Deep Neural Networks.

Nasim Rahaman, Devansh Arpit, Aristide Baratin, Felix Draxler, Min Lin, Fred A. Hamprecht, Yoshua Bengio, Aaron C. Courville

CoRR, 2018

MINE: Mutual Information Neural Estimation.

Ishmael Belghazi, Sai Rajeswar, Aristide Baratin, R. Devon Hjelm, Aaron C. Courville

CoRR, 2018

A3T: Adversarially Augmented Adversarial Training.

Akram Erraqabi, Aristide Baratin, Yoshua Bengio, Simon Lacoste-Julien

CoRR, 2018

Mutual Information Neural Estimation.

Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeswar, Sherjil Ozair, Yoshua Bengio, R. Devon Hjelm, Aaron C. Courville

Proceedings of the 35th International Conference on Machine Learning, 2018