2 citations · 3 across the 6 of their papers we have counts for
9 papers
Parallel Stochastic Gradient-Based Planning for World Models
Michael Psenka, Michael Rabbat, Aditi Krishnapriyan +2
World models simulate environment dynamics from raw sensory inputs like video. However, using them for planning can be challenging due to the vast and unstructured search space. We…
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…
Closing the Train-Test Gap in World Models for Gradient-Based Planning
Arjun Parthasarathy, Nimit Kalra, Rohun Agrawal +4
World models paired with model predictive control (MPC) can be trained offline on large-scale datasets of expert trajectories and enable generalization to a wide range of planning…
LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics
Randall Balestriero, Yann LeCun
Learning manipulable representations of the world and its dynamics is central to AI. Joint-Embedding Predictive Architectures (JEPAs) offer a promising blueprint, but lack of pract…
Gaussian Embeddings: How JEPAs Secretly Learn Your Data Density
Randall Balestriero, Nicolas Ballas, Mike Rabbat +1
Joint Embedding Predictive Architectures (JEPAs) learn representations able to solve numerous downstream tasks out-of-the-box. JEPAs combine two objectives: (i) a latent-space pred…
LLM-JEPA: Large Language Models Meet Joint Embedding Predictive Architectures
Hai Huang, Yann LeCun, Randall Balestriero
Large Language Model (LLM) pretraining, finetuning, and evaluation rely on input-space reconstruction and generative capabilities. Yet, it has been observed in vision that embeddin…