PGUDA uses pressure signals to train a teacher network that distills modality-invariant knowledge into an sEMG student via cross-modal distillation, reaching 58.08% cross-subject accuracy with only 5% labeled data for the teacher.
Deep subdomain adaptation network for image classification,
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A new evaluation framework using MMD on Biber features shows LLMs deviate from human linguistic distributions across registers, with closest models varying by register rather than size.
citing papers explorer
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PGUDA: Pressure-Guided Unsupervised Domain Adaptation with Cross-Modal Knowledge Distillation for sEMG-Based Gesture Recognition
PGUDA uses pressure signals to train a teacher network that distills modality-invariant knowledge into an sEMG student via cross-modal distillation, reaching 58.08% cross-subject accuracy with only 5% labeled data for the teacher.
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How Human-Like Are Large Language Models? A Register-Aware Linguistic Evaluation Framework
A new evaluation framework using MMD on Biber features shows LLMs deviate from human linguistic distributions across registers, with closest models varying by register rather than size.