arXiv (All)
open access
Signal-Noise Factorization Isolates Nuisance Variation into Removable Subspaces
Recent theoretical work identified fundamental properties of representation geometry that shape inference ability of deep neural networks. These include signal-noise factorization (SNF), the ability to segregate signal from noise, and signal-signal factorization (SSF), the ability to segregate task-
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
- An Analysis of Self-supervised Pre-training with Dependent Samples#971986
- Adaptive Subspace Modeling With Functional Tucker Decomposition#1104991
- Learning regularities in noise engages both neural predictive activity and representational changes#346662
- Learning task-specific subspaces via interventional post-training of speech foundation models#1061732
- Understanding Deep Learning via Entropy Space Theory#794087
Record · ID 1155686
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