5 citations · 6 across the 10 of their papers we have counts for
6 papers · 1 filter
LeVJEPA: Efficient & Scalable Video Pretraining without the Heuristics
Lukas Kuhn, Lucas Maes, Giuseppe Serra +4
Video carries the temporal structure of the physical world, yet learning representations from it has remained computationally expensive: prevailing self-supervised methods either p…
LpWM: A Case for Sparse Representations in World Models
Yilun Kuang, Yash Dagade, Quentin Le Lidec +3
Joint-embedding predictive architectures (JEPAs) learn latent dynamics for planning and avoid representation collapse by matching features to maximum-entropy distributions such as…
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…
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…
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…
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…