Retrieval from out-of-domain foundation models enables personalization of a lightweight transformer for stress detection, yielding +3.92% accuracy and +4.76% F1 gains on WESAD without user labels.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
verdicts
UNVERDICTED 3representative citing papers
RATs agents generate and solve their own exploratory tasks during play, distill successful code into a skill library, and reuse it to improve held-out task performance by 20.6 and 17.0 points on two benchmarks.
MAGELLAN augments LLM agents with online metacognitive LP prediction via semantic generalization to scale curriculum learning in open-ended goal spaces.
citing papers explorer
-
Retrieval-Augmented Personalization with Foundation Models for Wearable Stress Detection
Retrieval from out-of-domain foundation models enables personalization of a lightweight transformer for stress detection, yielding +3.92% accuracy and +4.76% F1 gains on WESAD without user labels.
-
Playful Agentic Robot Learning
RATs agents generate and solve their own exploratory tasks during play, distill successful code into a skill library, and reuse it to improve held-out task performance by 20.6 and 17.0 points on two benchmarks.
-
MAGELLAN: Metacognitive predictions of learning progress guide autotelic LLM agents in large goal spaces
MAGELLAN augments LLM agents with online metacognitive LP prediction via semantic generalization to scale curriculum learning in open-ended goal spaces.