arXiv (All)
open access
Escaping the Capacity Ceiling: Routing on the Stiefel Manifold for Bilinear SPD Layers
Deep networks on the symmetric positive-definite (SPD) manifold promise expressive representations by encoding data geometry as an inductive bias, but stacking BiMap layers with the standard ReEig nonlinearity often adds no capacity: on real, preconditioned EEG data, ReEig rarely activates, so the s
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
- Nested Inductive Bias Framework for SPD Manifold Learning#927754
- Higher Structures in Deep Learning#819918
- Green-ELM: Efficient Analytic Learning via High-Dimensional Random Projections#1014670
- How Bregman Divergences Shape Shampoo#1218269
- How Bregman Divergences Shape Shampoo#1210317
Record · ID 1187419
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