HAL (France)
open access
Efficient Artificial Intelligence Based On Teacher-Student Paradigm : Understanding Low-Rank Learning and Improving Channel Decoding
This thesis addresses one question through the teacher--student paradigm: when can a small model do the work of a large one? Its central principle: the sample and parameter efficiency of a learned model is set by the approximation capacity of the task and by the route through which the student absor
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
- Knowledge Distillation under Teacher Misspecification: An Order-Parameter Analysis of the Gap between Teacher Mimicry and Task Performance#794254
- Knowledge Distillation under Teacher Misspecification: An Order-Parameter Analysis of the Gap between Teacher Mimicry and Task Performance#790235
- Prior knowledge and statistical models of learning#1283690
- Desafíos de la integración de la inteligencia artificial en la educación#658267
- The learner who does not learn: when optimizing a pedagogical metric degrades LLM tutoring#1249549
Record · ID 1329284
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