5 citations · 6 across the 10 of their papers we have counts for
5 papers · 1 filter
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…
On the Importance of Embedding Norms in Self-Supervised Learning
Andrew Draganov, Sharvaree Vadgama, Sebastian Damrich +4
Self-supervised learning (SSL) allows training data representations without a supervised signal and has become an important paradigm in machine learning. Most SSL methods employ th…
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…