Zobrazeno 1 - 10
of 136
pro vyhledávání: '"Shai Fine"'
Autor:
Shira Barzilay, Shai Fine, Shannel Akhavan, Liat Haruvi-Catalan, Alan Apter, Anat Brunstein-Klomek, Lior Carmi, Mishael Zohar, Inbar Kinarty, Talia Friedman, Silvana Fennig
Publikováno v:
JMIR Research Protocols, Vol 12, p e46464 (2023)
BackgroundSuicide is the second leading cause of death in adolescents, and self-harm is one of the strongest predictors of death by suicide. The rates of adolescents presenting to emergency departments (EDs) for suicidal thoughts and behaviors (STBs)
Externí odkaz:
https://doaj.org/article/3f012bff4164402d9eb694691265d361
Autor:
Dana Zalman (Oshri), Shai Fine
Publikováno v:
Entropy, Vol 25, Iss 10, p 1468 (2023)
Variational inference provides a way to approximate probability densities through optimization. It does so by optimizing an upper or a lower bound of the likelihood of the observed data (the evidence). The classic variational inference approach sugge
Externí odkaz:
https://doaj.org/article/39d68bed7389427b8853025b341b825e
Autor:
Shani Cohen, Zeev Waks, Jordan J. Elm, Mark Forrest Gordon, Igor D. Grachev, Leehee Navon-Perry, Shai Fine, Iris Grossman, Spyros Papapetropoulos, Juha-Matti Savola
Publikováno v:
BMC Medical Informatics and Decision Making, Vol 18, Iss 1, Pp 1-10 (2018)
Abstract Background A growing number of clinical trials use various sensors and smartphone applications to collect data outside of the clinic or hospital, raising the question to what extent patients comply with the unique requirements of remote stud
Externí odkaz:
https://doaj.org/article/134b3a97a1744880bb450ac95d1ff202
Autor:
Dana Oshri Zalman, Shai Fine
Publikováno v:
2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e980471fb241cb4f1a75adcbe448805c
https://doi.org/10.1109/icmla55696.2022.00136
https://doi.org/10.1109/icmla55696.2022.00136
Autor:
Aviv Peled, Shai Fine
Publikováno v:
2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8f71bac04944ae96740a25f02fc0c821
https://doi.org/10.1109/icmla52953.2021.00046
https://doi.org/10.1109/icmla52953.2021.00046
Autor:
Balcan, Maria-Florina1 ninamf@cs.cmu.edu, Dick, Travis2 tdick@google.com, Sandholm, Tuomas3 sandholm@cs.cmu.edu, Vitercik, Ellen4 vitercik@stanford.edu
Publikováno v:
Journal of the ACM. Apr2024, Vol. 71 Issue 2, p1-73. 73p.
Publikováno v:
International Journal on Software Tools for Technology Transfer. 13:247-261
Closing the feedback loop from coverage data to the stimuli generator is one of the main challenges in the verification process. Typically, verification engineers with deep domain knowledge manually prepare a set of stimuli generation directives for
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d43ae826eacc40171f2d29f1bcd9db0b
https://doi.org/10.1007/s10009-010-0160-z
https://doi.org/10.1007/s10009-010-0160-z
Autor:
Ronen Meir Tamir, Shuai Cao, Guilherme Safioti, Ron Kamienchick, Dimitris Alevras, Iris Grossman, Dilip Chary, Thomas Ferro, Premprakaash Viswanathan, Lena Granovsky, Nancy S Yacovzada, Shai Fine
Publikováno v:
Journal of Allergy and Clinical Immunology. 141:AB222
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d3246357e5f13f7471f96ea17a955de3
https://doi.org/10.1016/j.jaci.2017.12.703
https://doi.org/10.1016/j.jaci.2017.12.703
Publikováno v:
International Journal on Software Tools for Technology Transfer. 11:291-305
Reaching hard-to-reach coverage events is a difficult task that requires both time and expertise. Data-driven coverage directed generation (CDG) can assist in the task when the coverage events are part of a structured coverage model, but is a priori
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6c2ddc0f9b920c7cd699119116622bd2
https://doi.org/10.1007/s10009-009-0119-0
https://doi.org/10.1007/s10009-009-0119-0
Publikováno v:
Theoretical Computer Science. 404(3):219-234
Automated regression suites are essential in developing large applications, while maintaining reasonable quality and timetables. The main argument against the automation of regression suites, in addition to the cost of creation and maintenance, is th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1ca19d3c9947131c159f73db38afdfbd