11 citations · 12 across the 4 of their papers we have counts for
5 papers
FLEX-WAM: Flexible Block-Causal World-Action Models for Long-Horizon Imagination and Planning
R. Khorrambakht, Joseph Amigo, Félix Lebel +4
World--action models (WAMs) promise a unified model that predicts action-conditioned futures, generates feasible actions, and supports planning in imagination. However, existing jo…
PEIRA: Learning Predictive Encoders through Inter-View Regressor Alignment
Michael Arbel, Basile Terver, Jean Ponce
Non-contrastive self-supervised learning (SSL) is an effective framework for predictive representation learning, but popular (and in practice effective) methods such as SimSiam, BY…
Value-guided action planning with JEPA world models
Matthieu Destrade, Oumayma Bounou, Quentin Le Lidec +2
Building deep learning models that can reason about their environment requires capturing its underlying dynamics. Joint-Embedded Predictive Architectures (JEPA) provide a promising…
What Drives Success in Physical Planning with Joint-Embedding Predictive World Models?
Basile Terver, Tsung-Yen Yang, Jean Ponce +2
A long-standing challenge in AI is to develop agents capable of solving a wide range of physical tasks and generalizing to new, unseen tasks and environments. A popular recent appr…
Revisiting Feature Prediction for Learning Visual Representations from Video
Adrien Bardes, Quentin Garrido, Jean Ponce +5
This paper explores feature prediction as a stand-alone objective for unsupervised learning from video and introduces V-JEPA, a collection of vision models trained solely using a f…