9 citations · 11 across the 10 of their papers we have counts for
4 papers · 1 filter
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…
Back to the Features: DINO as a Foundation for Video World Models
Federico Baldassarre, Marc Szafraniec, Basile Terver +6
We present DINO-world, a powerful generalist video world model trained to predict future frames in the latent space of DINOv2. By leveraging a pre-trained image encoder and trainin…
Embodied AI Agents: Modeling the World
Pascale Fung, Yoram Bachrach, Asli Celikyilmaz +18
This paper describes our research on AI agents embodied in visual, virtual or physical forms, enabling them to interact with both users and their environments. These agents, which…
Dual Perspectives on Non-Contrastive Self-Supervised Learning
Jean Ponce, Basile Terver, Martial Hebert +1
The {\em stop gradient} and {\em exponential moving average} iterative procedures are commonly used in non-contrastive approaches to self-supervised learning to avoid representatio…