Rectified LpJEPA: Joint-Embedding Predictive Architectures with Sparse and Maximum-Entropy Representations
arXiv:2602.01456v2 Announce Type: replace Abstract: Joint-Embedding Predictive Architectures (JEPA) learn view-invariant representations and admit projection-based distribution matching for collapse prevention. Existing approaches regularize representations towards isotropic Gaussian distributions, but inherently favor dense representations and fail to capture the key property of sparsity observed in efficient representations. We introduce Rectified Distribution Matching Regularization (RDMReg), a sliced two-sample distribution-matching loss that aligns representations to a Rectified Generaliz
This research builds on recent advancements in Joint-Embedding Predictive Architectures (JEPA), addressing limitations in existing models' ability to handle sparse representations, a critical area for more efficient AI learning.
Improved JEPA models with sparse representations can lead to more efficient, less data-hungry AI systems, directly impacting the development costs and capabilities of advanced AI models.
The introduction of RDMReg and the Rectified LpJEPA framework offers a new method for AI models to learn view-invariant, sparse representations, potentially leading to more robust and generalized AI.
- · AI research institutions
- · Developers of foundational AI models
- · Cloud AI service providers
- · Organizations relying on dense, inefficient AI models
- · AI approaches heavily dependent on massive datasets
More resource-efficient AI model training becomes possible.
Reduced compute costs for developing advanced AI, potentially democratizing access to powerful models.
Accelerated development of sophisticated AI agents and autonomous systems due to improved learning efficiency.
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Read at arXiv cs.LG