[Rectified LpJEPA]: JEPA - Principled Sparsity through Distribution Matching. JEPA, V-JEPA2, VL-JEPA скачать в хорошем качестве

[Rectified LpJEPA]: JEPA - Principled Sparsity through Distribution Matching. JEPA, V-JEPA2, VL-JEPA

JEPA - Joint Embedded Predictive Architecture. [Rectified LpJEPA]: JEPA - Principled Sparsity through Distribution Matching. World Models and JEPA, V-JEPA 2, VL-JEPA. In today’s deep dive, we explore Rectified LpJEPA, a self-supervised learning framework that challenges the status quo of dense, computationally "loud" embeddings by prioritizing principled sparsity and non-negative representations. While modern AI often relies on massive, biologically inefficient Gaussian distributions, this method introduces Rectified Distribution Matching (RDMReg) to fundamentally reshape the geometry of the latent space. By leveraging the Sliced Wasserstein Distance, the model aligns its feature distributions with a Rectified Generalized Gaussian target—a move designed to maximize information entropy while enforcing an efficient L0 norm. We will be breaking down the mathematical transition from density to sparsity, the mechanics of the Lp norm in this rectification process, and why matching distributions is the key to creating AI that mimics the selective, energy-efficient coding found in biological systems.