Search Results for author: Aristide Baratin

Found 9 papers, 2 papers with code

Learnability and Expressiveness in Self-Supervised Learning

no code implementations29 Sep 2021 Yuchen Lu, Zhen Liu, Alessandro Sordoni, Aristide Baratin, Romain Laroche, Aaron Courville

In this work, we argue that representations induced by self-supervised learning (SSL) methods should both be expressive and learnable.

Data Augmentation Self-Supervised Learning

On the Regularity of Attention

no code implementations10 Feb 2021 James Vuckovic, Aristide Baratin, Remi Tachet des Combes

Attention is a powerful component of modern neural networks across a wide variety of domains.

A Mathematical Theory of Attention

no code implementations6 Jul 2020 James Vuckovic, Aristide Baratin, Remi Tachet des Combes

Attention is a powerful component of modern neural networks across a wide variety of domains.

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

no code implementations19 Oct 2018 Brady Neal, Sarthak Mittal, Aristide Baratin, Vinayak Tantia, Matthew Scicluna, Simon Lacoste-Julien, Ioannis Mitliagkas

The bias-variance tradeoff tells us that as model complexity increases, bias falls and variances increases, leading to a U-shaped test error curve.

Mutual Information Neural Estimation

no code implementations ICML 2018 Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeshwar, Sherjil Ozair, Yoshua Bengio, Aaron Courville, Devon Hjelm

We argue that the estimation of mutual information between high dimensional continuous random variables can be achieved by gradient descent over neural networks.

General Classification

On the Spectral Bias of Neural Networks

2 code implementations ICLR 2019 Nasim Rahaman, Aristide Baratin, Devansh Arpit, Felix Draxler, Min Lin, Fred A. Hamprecht, Yoshua Bengio, Aaron Courville

Neural networks are known to be a class of highly expressive functions able to fit even random input-output mappings with $100\%$ accuracy.

MINE: Mutual Information Neural Estimation

18 code implementations12 Jan 2018 Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeswar, Sherjil Ozair, Yoshua Bengio, Aaron Courville, R. Devon Hjelm

We argue that the estimation of mutual information between high dimensional continuous random variables can be achieved by gradient descent over neural networks.

General Classification

A3T: Adversarially Augmented Adversarial Training

no code implementations12 Jan 2018 Akram Erraqabi, Aristide Baratin, Yoshua Bengio, Simon Lacoste-Julien

Recent research showed that deep neural networks are highly sensitive to so-called adversarial perturbations, which are tiny perturbations of the input data purposely designed to fool a machine learning classifier.

Adversarial Robustness General Classification

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