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pro vyhledávání: '"Stremmel, Joel"'
Autor:
Monajatipoor, Masoud, Yang, Jiaxin, Stremmel, Joel, Emami, Melika, Mohaghegh, Fazlolah, Rouhsedaghat, Mozhdeh, Chang, Kai-Wei
Large Language Models (LLMs) demonstrate remarkable versatility in various NLP tasks but encounter distinct challenges in biomedical due to the complexities of language and data scarcity. This paper investigates LLMs application in the biomedical dom
Externí odkaz:
http://arxiv.org/abs/2404.07376
Autor:
Stremmel, Joel, Saeedi, Ardavan, Hassanzadeh, Hamid, Batra, Sanjit, Hertzberg, Jeffrey, Murillo, Jaime, Halperin, Eran
Extractive question answering (QA) systems can enable physicians and researchers to query medical records, a foundational capability for designing clinical studies and understanding patient medical history. However, building these systems typically r
Externí odkaz:
http://arxiv.org/abs/2312.03567
Recent advances in large language models (LLMs) show potential for clinical applications, such as clinical decision support and trial recommendations. However, the GPT-4 LLM predicts an excessive number of ICD codes for medical coding tasks, leading
Externí odkaz:
http://arxiv.org/abs/2311.13735
Autor:
Stremmel, Joel, Hill, Brian L., Hertzberg, Jeffrey, Murillo, Jaime, Allotey, Llewelyn, Halperin, Eran
Publikováno v:
Proceedings of the 2nd Machine Learning for Health symposium, PMLR 193:218-258, 2022
While Transformer language models (LMs) are state-of-the-art for information extraction, long text introduces computational challenges requiring suboptimal preprocessing steps or alternative model architectures. Sparse attention LMs can represent lon
Externí odkaz:
http://arxiv.org/abs/2209.01174
Autor:
Stremmel, Joel, Singh, Arjun
Federated learning is a decentralized approach for training models on distributed devices, by summarizing local changes and sending aggregate parameters from local models to the cloud rather than the data itself. In this research we employ the idea o
Externí odkaz:
http://arxiv.org/abs/2005.04828
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