CogAdapt adapts clinical ECG foundation models to 3-lead wearable signals for cognitive load assessment via a LeadBridge adapter and ProFine progressive fine-tuning, outperforming scratch-trained models with macro-F1 of 0.626 and 0.768 on public datasets under leave-one-subject-out validation.
Delving deep into rectifiers: Surpass- ing human-level performance on imagenet classification
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DCT-based initialization and frequency truncation for self-attention improve accuracy and reduce overhead in Vision Transformers on standard benchmarks.
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CogAdapt: Transferring Clinical ECG Foundation Models to Wearable Cognitive Load Assessment via Lead Adaptation
CogAdapt adapts clinical ECG foundation models to 3-lead wearable signals for cognitive load assessment via a LeadBridge adapter and ProFine progressive fine-tuning, outperforming scratch-trained models with macro-F1 of 0.626 and 0.768 on public datasets under leave-one-subject-out validation.
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Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers
DCT-based initialization and frequency truncation for self-attention improve accuracy and reduce overhead in Vision Transformers on standard benchmarks.