9 citations · 11 across the 10 of their papers we have counts for
5 papers · 1 filter
H-JEPA: End-to-End Learning of Hierarchical World Models for Visual Planning
Wancong Zhang, Basile Terver, Michael Rabbat +2
Long-horizon planning with latent world models requires reasoning across timescales and levels of abstraction. Existing task-agnostic JEPA world models predict and plan at a single…
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…
Hierarchical Planning with Latent World Models
Wancong Zhang, Basile Terver, Artem Zholus +8
World models are a promising path to zero-shot embodied control through planning. However, existing world model planners struggle on long-horizon, multi-stage tasks: prediction err…
A Lightweight Library for Energy-Based Joint-Embedding Predictive Architectures
Basile Terver, Randall Balestriero, Megi Dervishi +8
We present EB-JEPA, an open-source library for learning representations and world models using Joint-Embedding Predictive Architectures (JEPAs). JEPAs learn to predict in represent…
Learning Latent Action World Models In The Wild
Quentin Garrido, Tushar Nagarajan, Basile Terver +3
Agents capable of reasoning and planning in the real world require the ability of predicting the consequences of their actions. While world models possess this capability, they mos…