Zobrazeno 1 - 10
of 97
pro vyhledávání: '"Benjamini, Yuval"'
Autor:
Slavutsky, Yuli, Benjamini, Yuval
Zero-shot learning methods typically assume that the new, unseen classes that are encountered at deployment, come from the same distribution as training classes. However, real-world scenarios often involve class distribution shifts (e.g., in age or g
Externí odkaz:
http://arxiv.org/abs/2311.18575
Autor:
Neuhof, Bitya, Benjamini, Yuval
Machine learning models are widely applied in various fields. Stakeholders often use post-hoc feature importance methods to better understand the input features' contribution to the models' predictions. The interpretation of the importance values pro
Externí odkaz:
http://arxiv.org/abs/2307.15361
Autor:
Ashiri-Prossner, Guy, Benjamini, Yuval
In this paper we discuss how to evaluate the differences between fitted logistic regression models across sub-populations. Our motivating example is in studying computerized diagnosis for learning disabilities, where sub-populations based on gender m
Externí odkaz:
http://arxiv.org/abs/2303.13330
Autor:
SORKIN, NIR, ZADOK, ROTEM, SAVINI, GIACOMO, KAN-TOR, YOAV, BENJAMINI, YUVAL, LEVINGER, ELIYA, BARDUGO, JUDITH, ABULAFIA, ADI
Publikováno v:
In American Journal of Ophthalmology September 2024 265:156-164
Correlation matrices are widely used to analyze the interdependence of variables in various real-world scenarios. Often, a perturbation in a few variables leads to mild differences in many correlation coefficients associated with these variables. We
Externí odkaz:
http://arxiv.org/abs/2111.07444
Autor:
Segers, Maartje H.M., Abulafia, Adi, Webers, Valentijn S.C., Verstraaten, Jan-Willem, Vandevenne, Magali M.S., Berendschot, Tos T.J.M., Kan-tor, Yoav, Benjamini, Yuval, van den Biggelaar, Frank J.H.M., Barrett, Graham D., Nuijts, Rudy M.M.A., Dickman, Mor M.
Publikováno v:
In American Journal of Ophthalmology June 2024 262:107-113
Autor:
Slavutsky, Yuli, Benjamini, Yuval
Publikováno v:
International Conference on Learning Representations (ICLR), 2021
Multiclass classifiers are often designed and evaluated only on a sample from the classes on which they will eventually be applied. Hence, their final accuracy remains unknown. In this work we study how a classifier's performance over the initial cla
Externí odkaz:
http://arxiv.org/abs/2010.15011
The difficulty of multi-class classification generally increases with the number of classes. Using data from a subset of the classes, can we predict how well a classifier will scale with an increased number of classes? Under the assumptions that the
Externí odkaz:
http://arxiv.org/abs/1712.09713
Publikováno v:
In Social Networks October 2022 71:70-79
Autor:
Kan-Tor, Yoav, Abulafia, Adi, Zadok, David, Kohnen, Thomas, Savini, Giacomo, Hoffer, Kenneth J., Benjamini, Yuval
Publikováno v:
Journal of Cataract & Refractive Surgery; Nov2024, Vol. 50 Issue 11, p1128-1134, 7p