arXiv (All)
open access
Learning Transfers: Kan Extensions of Topological Invariants of Learned Representations
When a learned representation is reused for a new task, which of its structure should survive? A task is a small category, a change of task is a functor $J$, and the invariants of a trained representation form a functor $F$. The left Kan extension $\operatorname{Lan}_JF$ is the invariant that the so
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
- Separating visual memories through learned action associations#385669
- Structural generalization and continual learning enabled by factorized entorhinal-hippocampal memory and entorhinal-parietal action circuits#1110897
- You Can't Have It Both Ways: Concept Entanglement Limits Diffusion Model Unlearning#1119210
- Storyboarding for Invention: Layering Modes for More Effective Transfer in a Multimodal Composition Classroom#682928
- The Trade-off Between Covariate Dependence and Latent Structure in Representation Learning#620651
Record · ID 1297167
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