5 citations · 5 across the 4 of their papers we have counts for
4 papers
An Attention Mechanism for Robust Multimodal Integration in a Global Workspace Architecture
Roland Bertin-Johannet, Lara Scipio, Leopold Maytié +1
Robust multimodal systems must remain effective when some modalities are noisy, degraded, or unreliable. Existing multimodal fusion methods often learn modality selection jointly w…
Multimodal Dreaming: A Global Workspace Approach to World Model-Based Reinforcement Learning
Léopold Maytié, Roland Bertin Johannet, Rufin VanRullen
Humans leverage rich internal models of the world to reason about the future, imagine counterfactuals, and adapt flexibly to new situations. In Reinforcement Learning (RL), world m…
Zero-shot cross-modal transfer of Reinforcement Learning policies through a Global Workspace
Léopold Maytié, Benjamin Devillers, Alexandre Arnold +1
Humans perceive the world through multiple senses, enabling them to create a comprehensive representation of their surroundings and to generalize information across domains. For in…
Semi-supervised Multimodal Representation Learning through a Global Workspace
Benjamin Devillers, Léopold Maytié, Rufin VanRullen
Recent deep learning models can efficiently combine inputs from different modalities (e.g., images and text) and learn to align their latent representations, or to translate signal…