Zobrazeno 1 - 10
of 65
pro vyhledávání: '"Amir Feder"'
Autor:
Ariel Goldstein, Avigail Grinstein-Dabush, Mariano Schain, Haocheng Wang, Zhuoqiao Hong, Bobbi Aubrey, Samuel A. Nastase, Zaid Zada, Eric Ham, Amir Feder, Harshvardhan Gazula, Eliav Buchnik, Werner Doyle, Sasha Devore, Patricia Dugan, Roi Reichart, Daniel Friedman, Michael Brenner, Avinatan Hassidim, Orrin Devinsky, Adeen Flinker, Uri Hasson
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-12 (2024)
Abstract Contextual embeddings, derived from deep language models (DLMs), provide a continuous vectorial representation of language. This embedding space differs fundamentally from the symbolic representations posited by traditional psycholinguistics
Externí odkaz:
https://doaj.org/article/b5d187486aaf4abfb9f4c8c3e4bdc2c8
Autor:
Ariel Goldstein, Avigail Grinstein-Dabush, Mariano Schain, Haocheng Wang, Zhuoqiao Hong, Bobbi Aubrey, Samuel A. Nastase, Zaid Zada, Eric Ham, Amir Feder, Harshvardhan Gazula, Eliav Buchnik, Werner Doyle, Sasha Devore, Patricia Dugan, Roi Reichart, Daniel Friedman, Michael Brenner, Avinatan Hassidim, Orrin Devinsky, Adeen Flinker, Uri Hasson
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-1 (2024)
Externí odkaz:
https://doaj.org/article/69055997d99a4a65bc9dcf640c150514
Autor:
Amir Feder, Katherine A. Keith, Emaad Manzoor, Reid Pryzant, Dhanya Sridhar, Zach Wood-Doughty, Jacob Eisenstein, Justin Grimmer, Roi Reichart, Margaret E. Roberts, Brandon M. Stewart, Victor Veitch, Diyi Yang
Publikováno v:
Transactions of the Association for Computational Linguistics, Vol 10, Pp 1138-1158 (2022)
AbstractA fundamental goal of scientific research is to learn about causal relationships. However, despite its critical role in the life and social sciences, causality has not had the same importance in Natural Language Processing (NLP), which has tr
Externí odkaz:
https://doaj.org/article/ce20dd42414b496493ce8c27322a7d13
Publikováno v:
Computational Linguistics, Vol 47, Iss 2, Pp 333-386 (2021)
AbstractUnderstanding predictions made by deep neural networks is notoriously difficult, but also crucial to their dissemination. As all machine learning–based methods, they are as good as their training data, and can also capture unwanted biases.
Externí odkaz:
https://doaj.org/article/695c189dcd6c464bb43626363309264e
Publikováno v:
Transactions of the Association for Computational Linguistics, Vol 9, Pp 1355-1373 (2021)
AbstractRecent improvements in the predictive quality of natural language processing systems are often dependent on a substantial increase in the number of model parameters. This has led to various attempts of compressing such models, but existing me
Externí odkaz:
https://doaj.org/article/80203053f8334c5c92156ba86cbec677
Publikováno v:
Computational Linguistics, Vol 46, Iss 3 (2020)
Sports competitions are widely researched in computer and social science, with the goal of understanding how players act under uncertainty. Although there is an abundance of computational work on player metrics prediction based on past performance, v
Externí odkaz:
https://doaj.org/article/66b43b8683cc48249e038c9c29c54721
Autor:
Ariel Goldstein, Eric Ham, Samuel A. Nastase, Zaid Zada, Avigail Grinstein-Dabus, Bobbi Aubrey, Mariano Schain, Harshvardhan Gazula, Amir Feder, Werner Doyle, Sasha Devore, Patricia Dugan, Daniel Friedman, Michael Brenner, Avinatan Hassidim, Orrin Devinsky, Adeen Flinker, Omer Levy, Uri Hasson
Deep language models (DLMs) provide a novel computational paradigm for how the brain processes natural language. Unlike symbolic, rule-based models described in psycholinguistics, DLMs encode words and their context as continuous numerical vectors. T
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7604da7bf8d052d090e3af10a363ae1a
https://doi.org/10.1101/2022.07.11.499562
https://doi.org/10.1101/2022.07.11.499562
Autor:
Doron Stupp, Ronnie Barequet, I-Ching Lee, Eyal Oren, Amir Feder, Ayelet Benjamini, Avinatan Hassidim, Yossi Matias, Eran Ofek, Alvin Rajkomar
Physicians record their detailed thought-processes about diagnoses and treatments as unstructured text in a section of a clinical note called the assessment and plan. This information is more clinically rich than structured billing codes assigned for
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::718905af29f1c732331b5365ed5c29e6
https://doi.org/10.1101/2022.04.13.22273438
https://doi.org/10.1101/2022.04.13.22273438
Autor:
Ariel Goldstein, Avigail Dabush, Bobbi Aubrey, Mariano Schain, Samuel A. Nastase, Zaid Zada, Eric Ham, Zhuoqiao Hong, Amir Feder, Harshvardhan Gazula, Eliav Buchnik, Werner Doyle, Sasha Devore, Patricia Dugan, Daniel Friedman, Michael Brenner, Avinatan Hassidim, Orrin Devinsky, Adeen Flinker, Uri Hasson
Contextual embeddings, derived from deep language models (DLMs), provide a continuous vectorial representation of language. This embedding space differs fundamentally from the symbolic representations posited by traditional psycholinguistics. Do lang
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5fb35a499743f6ed8f4b93615f627765
https://doi.org/10.1101/2022.03.01.482586
https://doi.org/10.1101/2022.03.01.482586
Publikováno v:
Computational Linguistics. 46:667-712
Sports competitions are widely researched in computer and social science, with the goal of understanding how players act under uncertainty. While there is an abundance of computational work on player metrics prediction based on past performance, very
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a3d59652dc8a68a5a38c109429a27377
https://doi.org/10.1162/coli_a_00383
https://doi.org/10.1162/coli_a_00383