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of 9
pro vyhledávání: '"Goldwasser, Jeremy"'
Autor:
Goldwasser, Jeremy, Hooker, Giles
Feature attributions are ubiquitous tools for understanding the predictions of machine learning models. However, the calculation of popular methods for scoring input variables such as SHAP and LIME suffers from high instability due to random sampling
Externí odkaz:
http://arxiv.org/abs/2401.15800
Autor:
Yang, Rui, Zeng, Qingcheng, You, Keen, Qiao, Yujie, Huang, Lucas, Hsieh, Chia-Chun, Rosand, Benjamin, Goldwasser, Jeremy, Dave, Amisha D, Keenan, Tiarnan D. L., Chew, Emily Y, Radev, Dragomir, Lu, Zhiyong, Xu, Hua, Chen, Qingyu, Li, Irene
This study introduces Ascle, a pioneering natural language processing (NLP) toolkit designed for medical text generation. Ascle is tailored for biomedical researchers and healthcare professionals with an easy-to-use, all-in-one solution that requires
Externí odkaz:
http://arxiv.org/abs/2311.16588
Autor:
Goldwasser, Jeremy, Hooker, Giles
Shapley values are among the most popular tools for explaining predictions of blackbox machine learning models. However, their high computational cost motivates the use of sampling approximations, inducing a considerable degree of uncertainty. To sta
Externí odkaz:
http://arxiv.org/abs/2310.07672
Autor:
Li, Irene, You, Keen, Qiao, Yujie, Huang, Lucas, Hsieh, Chia-Chun, Rosand, Benjamin, Goldwasser, Jeremy, Radev, Dragomir
The Electronic Health Record (EHR) is an essential part of the modern medical system and impacts healthcare delivery, operations, and research. Unstructured text is attracting much attention despite structured information in the EHRs and has become a
Externí odkaz:
http://arxiv.org/abs/2204.06604
Autor:
Li, Irene, Pan, Jessica, Goldwasser, Jeremy, Verma, Neha, Wong, Wai Pan, Nuzumlalı, Muhammed Yavuz, Rosand, Benjamin, Li, Yixin, Zhang, Matthew, Chang, David, Taylor, R. Andrew, Krumholz, Harlan M., Radev, Dragomir
Electronic health records (EHRs), digital collections of patient healthcare events and observations, are ubiquitous in medicine and critical to healthcare delivery, operations, and research. Despite this central role, EHRs are notoriously difficult t
Externí odkaz:
http://arxiv.org/abs/2107.02975
In the era of single-cell sequencing, there is a growing need to extract insights from data with clustering methods. Here, we introduce Forest Fire Clustering, an efficient and interpretable method for cell-type discovery from single-cell data. Fores
Externí odkaz:
http://arxiv.org/abs/2103.11802
Akademický článek
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Autor:
Li, Irene, Pan, Jessica, Goldwasser, Jeremy, Verma, Neha, Wong, Wai Pan, Nuzumlalı, Muhammed Yavuz, Rosand, Benjamin, Li, Yixin, Zhang, Matthew, Chang, David, Taylor, R. Andrew, Krumholz, Harlan M., Radev, Dragomir
Publikováno v:
In Computer Science Review November 2022 46
Akademický článek
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