arXiv (All)
open access
Geometric Characterisation and Structured Trajectory Surrogates for Clinical Dataset Condensation
Dataset condensation constructs compact synthetic datasets that retain the training utility of large real-world datasets, enabling efficient model development and potentially supporting downstream research in governed domains such as healthcare. Trajectory matching (TM) is a widely used condensation
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
- Geometric Characterisation and Structured Trajectory Surrogates for Clinical Dataset Condensation#126528
- GENERALIZABLE DEEP LEARNING APPROACHES FOR COMPUTATIONAL DRUG DISCOVERY#396668
- An overview of self-supervised deep learning applications to molecular data.#382568
- Text Knows What, Tables Know When: Clinical Timeline Reconstruction via Retrieval-Augmented Multimodal Alignment#187295
- Revitalizing Medical Time Series with Vision-Informed Retrieval: A Vision-Language Perspective#1128913
Record · ID 1296411
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