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pro vyhledávání: '"Parikh, Soham"'
Autor:
Bahdanau, Dzmitry, Gontier, Nicolas, Huang, Gabriel, Kamalloo, Ehsan, Pardinas, Rafael, Piché, Alex, Scholak, Torsten, Shliazhko, Oleh, Tremblay, Jordan Prince, Ghanem, Karam, Parikh, Soham, Tiwari, Mitul, Vohra, Quaizar
We present TapeAgents, an agent framework built around a granular, structured log tape of the agent session that also plays the role of the session's resumable state. In TapeAgents we leverage tapes to facilitate all stages of the LLM Agent developme
Externí odkaz:
http://arxiv.org/abs/2412.08445
Conversational NLU providers often need to scale to thousands of intent-classification models where new customers often face the cold-start problem. Scaling to so many customers puts a constraint on storage space as well. In this paper, we explore fo
Externí odkaz:
http://arxiv.org/abs/2305.07157
Conversational AI assistants are becoming popular and question-answering is an important part of any conversational assistant. Using relevant utterances as features in question-answering has shown to improve both the precision and recall for retrievi
Externí odkaz:
http://arxiv.org/abs/2004.03484
Publikováno v:
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (2018) Main track. Pages 4272-4278
The task of Reading Comprehension with Multiple Choice Questions, requires a human (or machine) to read a given passage, question pair and select one of the n given options. The current state of the art model for this task first computes a question-a
Externí odkaz:
http://arxiv.org/abs/1904.02651
When humans learn to perform a difficult task (say, reading comprehension (RC) over longer passages), it is typically the case that their performance improves significantly on an easier version of this task (say, RC over shorter passages). Ideally, w
Externí odkaz:
http://arxiv.org/abs/1904.02665
Autor:
Parikh, Soham D., Dave, Soham, Huang, Luping, Wang, Wenhu, Mukhopadhyay, Sharmila M., Mayes, Debra A.
Publikováno v:
In Materials Science & Engineering C March 2020 108
Autor:
Parikh, Soham, Davoudi, Anahita, Yu, Shun, Giraldo, Carolina, Schriver, Emily, Mowery, Danielle
Publikováno v:
JMIR Medical Informatics, Vol 9, Iss 2, p e21679 (2021)
BackgroundScientists are developing new computational methods and prediction models to better clinically understand COVID-19 prevalence, treatment efficacy, and patient outcomes. These efforts could be improved by leveraging documented COVID-19–rel
Externí odkaz:
https://doaj.org/article/711cb0bf55e84750a5ae6ae3a4397216
Akademický článek
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Akademický článek
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Autor:
Wehrle, Chase J., Talukder, Asif, Tien, Lillie, Parikh, Soham, Devarakonda, Aditya, Holsten, Steven B., Fox, Elizabeth D., Lawson, Andrew
Publikováno v:
The American Surgeon; February 2022, Vol. 88 Issue: 2 p267-272, 6p