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.
In defense of pseudo- labeling: An uncertainty-aware pseudo-label selection framework for semi-supervised learning.arXiv preprint arXiv:2101.06329, 2021
3 Pith papers cite this work, alongside 73 external citations. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
Bidirectional LLM-GNN co-teaching with round-based pseudo-label preference optimization outperforms golden-teacher baselines on few-shot TAG benchmarks by 3-8% absolute gains.
HG-DTGL integrates human gaze as an extra teacher in mean-teacher learning via GazeMix, MGP module and Gaze Loss, reporting superior segmentation across ten organs on multiple modalities.
citing papers explorer
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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.
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Beyond the Golden Teacher: Enhancing Graph Learning through LLM-GNN Co-teaching
Bidirectional LLM-GNN co-teaching with round-based pseudo-label preference optimization outperforms golden-teacher baselines on few-shot TAG benchmarks by 3-8% absolute gains.
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Human Gaze-based Dual Teacher Guidance Learning for Semi-Supervised Medical Image Segmentation
HG-DTGL integrates human gaze as an extra teacher in mean-teacher learning via GazeMix, MGP module and Gaze Loss, reporting superior segmentation across ten organs on multiple modalities.