677 citations · 915 across the 9 of their papers we have counts for
13 papers · 1 filter
Self-supervised multimodal neuroimaging yields predictive representations for a spectrum of Alzheimer's phenotypes
Alex Fedorov, Eloy Geenjaar, Lei Wu +7
Recent neuroimaging studies that focus on predicting brain disorders via modern machine learning approaches commonly include a single modality and rely on supervised over-parameter…
Implicit Regularization via Neural Feature Alignment
Aristide Baratin, Thomas George, César Laurent +4
We approach the problem of implicit regularization in deep learning from a geometrical viewpoint. We highlight a regularization effect induced by a dynamical alignment of the neura…
Deep Reinforcement and InfoMax Learning
Bogdan Mazoure, Remi Tachet des Combes, Thang Doan +2
We begin with the hypothesis that a model-free agent whose representations are predictive of properties of future states (beyond expected rewards) will be more capable of solving a…
An end-to-end approach for the verification problem: learning the right distance
Joao Monteiro, Isabela Albuquerque, Jahangir Alam +2
In this contribution, we augment the metric learning setting by introducing a parametric pseudo-distance, trained jointly with the encoder. Several interpretations are thus drawn f…
Attraction-Repulsion Actor-Critic for Continuous Control Reinforcement Learning
Thang Doan, Bogdan Mazoure, Moloud Abdar +3
Continuous control tasks in reinforcement learning are important because they provide an important framework for learning in high-dimensional state spaces with deceptive rewards, w…
Learning Representations by Maximizing Mutual Information Across Views
Philip Bachman, R Devon Hjelm, William Buchwalter
We propose an approach to self-supervised representation learning based on maximizing mutual information between features extracted from multiple views of a shared context. For exa…