arXiv (All)
open access
Shared Gaussianization: What Gaussian Regularizers Certify About Contrastive Learning, and What They Miss
What can a distribution-matching regularizer such as SIGReg in LeJEPA certify about contrastive learning? We study shared Gaussianization (SG), a characteristic-function Gaussianity test on the average of two normalized views, scaled by an independent $χ_d$ radius. Because disagreeing views shorten
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
- Adaptive Regularization via Extreme Value Distributions for Gaussian Graphical Models#972729
- Low-Dimensional Embeddings for Gaussian Kernels on Manifolds#1109229
- Compact Feed-Forward 3D Gaussians via Saliency-Guided Primitive Merging#1128631
- A Temporal Spatial Minimax Rate for Smoothly-Varying Distributions in Wasserstein Space#266215
- Wasserstein-Barycentric Interaction Fields for Spatial Factor Models: Evidence from Language-Model Representations#798305
Record · ID 1234666
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