arxiv
PublishedOctober 2, 2026 at 4:00 AM
Effects of Structural Allocation of Geometric Task Diversity in Linear Meta-Learning Models
Publisher summary· verbatim
arXiv:2509.18349v4 Announce Type: replace-cross Abstract: Meta-learning aims to leverage information across related tasks to improve prediction on unlabeled data for new tasks when only a small number of labeled observations are available ("few-shot" learning). Increased task diversity is often beli
Stay posted· Newsletter
Three short emails a week: Monday, Wednesday and Friday. Top movers, new models and the most-referenced story.
Your email is used only to send The Bubble Brief. Privacy policy
Discussion
No replies yet. Be first.
Related coverage
More from ARXIV
arxivBridging the Gap: Converting Read Text to Conversational Dialogue12marxivTruth for Believable AI: Expressed Doubt, Provenance, and Belief Revision as an Engineerable Stance12marxivRICE-Alpha: Reliability-Informed Correction with Event Graphs for LLM-Agent Stock Forecasting12marxivThe World Won't Wait: Evaluating and Teaching Agents Under a Live Clock12mThe Bubble Brief
MON · WED · FRIRead AI insights every Monday, Wednesday and Friday — top movers, new models, story of the week.
Your email is used only to send The Bubble Brief. Privacy policy
Originally published on arxiv ↗