Contrastive predictive coding pretraining combined with structured state space models yields the strongest ECG foundation models, with continued gains from scaling data to 11 million samples.
Training compute-optimal large language models
4 Pith papers cite this work. Polarity classification is still indexing.
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Direct sequence-to-sequence EMG-to-text conversion for silent articulation using a geometric representation of high-dimensional signals, without audio targets or time-alignment.
SMoA is a new PEFT adapter that uses block-wise Hadamard-modulated low-rank branches on spectral partitions to cover more pretrained spectral directions than standard LoRA under a smaller parameter budget.
Advanced language representations shape LLMs' schemas to improve knowledge activation and problem-solving.
citing papers explorer
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Pretraining Strategies and Scaling for ECG Foundation Models: A Systematic Study
Contrastive predictive coding pretraining combined with structured state space models yields the strongest ECG foundation models, with continued gains from scaling data to 11 million samples.
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Non-invasive electromyographic speech neuroprosthesis: a geometric perspective
Direct sequence-to-sequence EMG-to-text conversion for silent articulation using a geometric representation of high-dimensional signals, without audio targets or time-alignment.
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SMoA: Spectrum Modulation Adapter for Parameter-Efficient Fine-Tuning
SMoA is a new PEFT adapter that uses block-wise Hadamard-modulated low-rank branches on spectral partitions to cover more pretrained spectral directions than standard LoRA under a smaller parameter budget.
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Shaping Schema via Language Representation as the Next Frontier for LLM Intelligence Expanding
Advanced language representations shape LLMs' schemas to improve knowledge activation and problem-solving.