arXiv (All)
open access
Mask-Aware Execution for Efficient JEPA Training
Joint Embedding Predictive Architectures (JEPAs) are becoming a core representation-learning primitive and a building block for latent world models across vision, video, audio, brain dynamics, and time series. Despite (potential of) wide deployment, current JEPA training pipelines are inefficient: e
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Mask-Aware Execution for Efficient JEPA Training#1028673
- Subspace-Decomposed JEPAs: Disentangling Progression and Content in Latent World Models#1011117
- Orthogonal JEPA: Factorized Predictive States for Latent World Models#1052275
- Benchmarking EEG Foundation Models at Scale: Lessons from 20,000 Evaluations#1117074
- MotionJEPA: Preventing Temporal Feature Collapse by Capturing Visual Changes in Latent Space#1050072
Record · ID 1037357
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive