arXiv (All)
open access
Feature Superposition in Neural Networks: From Theory to Practice
Superposition refers to neural networks representing more features than they have dimensions. It offers a possible explanation for polysemantic neurons and motivates methods for recovering interpretable features from neural activations. Theoretical models typically start with a given set of input fe
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
- Emergent phases of superposition: from partial to full representation#1131297
- Functional compatibility as a determinant of persistent neural learning#1141628
- A Theory of Epistemic Supererogation#748369
- Threshold-Based Early Stopping of Accumulations in Neural Networks with Binary Activation#432536
- Born Again! The Born Rule as a feature of superposition#706103
Record · ID 974943
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