Zobrazeno 1 - 10
of 131
pro vyhledávání: '"Yoav Goldberg"'
Autor:
Hillel Taub-Tabib, Yosi Shamay, Micah Shlain, Menny Pinhasov, Mark Polak, Aryeh Tiktinsky, Sigal Rahamimov, Dan Bareket, Ben Eyal, Moriya Kassis, Yoav Goldberg, Tal Kaminski Rosenberg, Simon Vulfsons, Maayan Ben Sasson
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-15 (2024)
Abstract Differential diagnosis is a crucial aspect of medical practice, as it guides clinicians to accurate diagnoses and effective treatment plans. Traditional resources, such as medical books and services like UpToDate, are constrained by manual c
Externí odkaz:
https://doaj.org/article/fbaeeddda66843eabe5f1cc0e9e5a3ea
Publikováno v:
Transactions of the Association for Computational Linguistics, Vol 9, Pp 691-706 (2021)
We explore few-shot learning (FSL) for relation classification (RC). Focusing on the realistic scenario of FSL, in which a test instance might not belong to any of the target categories (none-of-the-above, [NOTA]), we first revisit the recent popular
Externí odkaz:
https://doaj.org/article/02c5a491c14a4dedb6937b1b41a6467c
Autor:
Yanai Elazar, Nora Kassner, Shauli Ravfogel, Abhilasha Ravichander, Eduard Hovy, Hinrich Schütze, Yoav Goldberg
Publikováno v:
Transactions of the Association for Computational Linguistics, Vol 9, Pp 1012-1031 (2021)
AbstractConsistency of a model—that is, the invariance of its behavior under meaning-preserving alternations in its input—is a highly desirable property in natural language processing. In this paper we study the question: Are Pretrained Language
Externí odkaz:
https://doaj.org/article/c20cd42e8e454ec39e1cb1d0b076a64a
Publikováno v:
Transactions of the Association for Computational Linguistics, Vol 9, Pp 1047-1060 (2021)
AbstractLanguage models trained on billions of tokens have recently led to unprecedented results on many NLP tasks. This success raises the question of whether, in principle, a system can ever “understand” raw text without access to some form of
Externí odkaz:
https://doaj.org/article/01b20bd81c594f82968f4a363577d1ef
Publikováno v:
Transactions of the Association for Computational Linguistics, Vol 9, Pp 160-175 (2021)
AbstractA growing body of work makes use of probing in order to investigate the working of neural models, often considered black boxes. Recently, an ongoing debate emerged surrounding the limitations of the probing paradigm. In this work, we point ou
Externí odkaz:
https://doaj.org/article/05ad2f8019bb49bab8263a2aaf167370
Autor:
Alon Jacovi, Yoav Goldberg
Publikováno v:
Transactions of the Association for Computational Linguistics, Vol 9, Pp 294-310 (2021)
AbstractWe find that the requirement of model interpretations to be faithful is vague and incomplete. With interpretation by textual highlights as a case study, we present several failure cases. Borrowing concepts from social science, we identify tha
Externí odkaz:
https://doaj.org/article/83791b8f70c549c9be83f595a4058e75
Publikováno v:
Computational Linguistics, Vol 43, Iss 2 (2017)
We introduce a greedy transition-based parser that learns to represent parser states using recurrent neural networks. Our primary innovation that enables us to do this efficiently is a new control structure for sequential neural networks—the stack
Externí odkaz:
https://doaj.org/article/c93ece51f0a348af86205d06adb3e8a6
Autor:
Shaked Launer-Wachs, Hillel Taub-Tabib, Jennie Tokarev Madem, Orr Bar-Natan, Yoav Goldberg, Yosi Shamay
Publikováno v:
Journal of Biomedical Informatics. 142:104383
Publikováno v:
Machine Learning.
Autor:
Danna Niezni, Hillel Taub-Tabib, Yuval Harris, Hagit Sason-Bauer, Yakir Amrusi, Dana Azagury, Maytal Avrashami, Shaked Launer-Wachs, Jon Borchardt, M Kusold, Aryeh Tiktinsky, Tom Hope, Yoav Goldberg, Yosi Shamay
Drug combination therapy is a main pillar of cancer therapy but the formation of an effective combinatorial standard of care (SOC) can take many years and its length of development is increasing with complexity of treatment. In this paper, we develop
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ec39081c65122e24e8f7d4a83cc4f11b
https://doi.org/10.1101/2022.05.03.490286
https://doi.org/10.1101/2022.05.03.490286