New research outlines sample requirements for cross-domain learning

A new research paper published on arXiv explores the sample requirements for effective learning across different domains. The study establishes criteria for per-domain sample sufficiency, revealing an inverse linear scaling law between the number of training domains and the samples needed per domain. This work also demonstrates a strong connection between in-domain learning and out-of-domain generalization, offering theoretical guidance for dataset construction and evaluation. AI

IMPACT Provides theoretical guidance for assessing dataset adequacy and constructing datasets for improved generalization.

RANK_REASON The item is a research paper published on arXiv discussing theoretical aspects of machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

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