5 citations · 5 across the 3 of their papers we have counts for
7 papers
LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels
Lucas Maes, Quentin Le Lidec, Damien Scieur +2
Joint Embedding Predictive Architectures (JEPAs) offer a compelling framework for learning world models in compact latent spaces, yet existing methods remain fragile, relying on co…
Causal-JEPA: Learning World Models through Object-Level Latent Masking
Heejeong Nam, Quentin Le Lidec, Lucas Maes +2
World models require robust relational understanding to support prediction, reasoning, and control. While object-centric representations provide a useful abstraction, they are not…
stable-worldmodel: A Platform for Reproducible World Modeling Research and Evaluation
Lucas Maes, Quentin Le Lidec, Luiz Facury +9
World models are central to building agents that can reason, plan, and generalize beyond their training data. However, research on world models is currently fragmented, with dispar…
stable-worldmodel-v1: Reproducible World Modeling Research and Evaluation
Lucas Maes, Quentin Le Lidec, Dan Haramati +4
World Models have emerged as a powerful paradigm for learning compact, predictive representations of environment dynamics, enabling agents to reason, plan, and generalize beyond di…
stable-pretraining-v1: Foundation Model Research Made Simple
Randall Balestriero, Hugues Van Assel, Sami BuGhanem +1
Foundation models and self-supervised learning (SSL) have become central to modern AI, yet research in this area remains hindered by complex codebases, redundant re-implementations…
Understanding Adam Requires Better Rotation Dependent Assumptions
Tianyue H. Zhang, Lucas Maes, Alan Milligan +5
Despite its widespread adoption, Adam's advantage over Stochastic Gradient Descent (SGD) lacks a comprehensive theoretical explanation. This paper investigates Adam's sensitivity t…