Bonjour à tous,

Vous êtes tous et toutes cordialement invité.es à assister à la présentation de projet du prédoc III de Lucas Maes, le 28 août à 10h (mode hybride).

Titre : Towards Efficient Self-Supervised World Models

Date: jeudi 28 août à 10h.

Location: Espace de cotravail, Mila, 6650 (1er étage)

Jury

Résumé

World models have recently attracted considerable attention. However,progress remains limited by divergent motivations, heterogeneous applications, and inconsistent formalisms. Moreover, the absence of astandardized evaluation protocol further hinders iteration and meaningfulcomparison across approaches. In this work, we take steps toward unifyingthe field and addressing its long-standing challenges. First, we propose a standard evaluation protocol for world models that measures the robustnessof each model component under environmental perturbations. Using thisprotocol, we assess the robustness of DINO-WM, a recently proposed approach. Second, we introduce Relational Representation Learning (RRL), a new paradigm for representation learning based on predicting inter-samplerelationships. RRL provides a principled explanation for puzzling phenomenain self-supervised learning, like the role of projector networks. Wefurther discuss how RRL could be applied to a major open problem in worldmodels: hierarchical planning. Finally, we outline future directions forimproving world models toward the broader goal of Autonomous Machine Intelligence (AMI).