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EPIClear: Exploiting Domain-Specific Features for Epistasis Detection Acceleration on Tensor Cores
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Comparing representations of long clinical texts for the task of patient note-identification
No full textThis study investigates the task of patient-note identification, focusing on learning effective representations from clinical texts to accurately match anonymized notes to their corresponding patients. The authors explore various embedding methods and pooling strategies, ultimately demonstrating that BERT-based models with sliding window mechanisms and mean_max pooling achieve the highest accuracy for this task. By comparing different approaches across two datasets (MIMIC-III and Necker hospital data warehouse), the research highlights the importance of representation learning and aggregation strategies in optimizing patient-note identification and enhancing patient-level modeling
Full SiC Three-Level T-Type Quasi-Z Source Inverter as Grid-Forming Unit in Islanded Nanogrid
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ORPAILLEUR & SyNaLP at CLEF 2024 Task 2: Good Old Cross Validation for Large Language Models Yields the Best Humorous Detection
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