arXiv (All)
open access
Any-Dimensional Learning by Sampling
Many machine learning models are defined for inputs of different sizes, such as point clouds containing different numbers of points, sequences of tokens of different lengths, and graphs on different numbers of nodes. Such models are trained on finitely many examples of necessarily limited sizes. How
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
- Any-Dimensional Learning by Sampling#349615
- How Many Samples Are Enough for Learning Across Domains?#1144303
- JIVE: Jacobian-Informed Volume Expansion for Diverse Generative Sampling#1118979
- Graph neural networks for sampling-invariant embeddings of organized signal sets#1130776
- The General Theory of Localization Methods#1069805
Record · ID 986294
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