Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Niv Giladi"'
Publikováno v:
Sensors, Vol 22, Iss 15, p 5540 (2022)
Predicting not only the target but also an accurate measure of uncertainty is important for many machine learning applications, and in particular, safety-critical ones. In this work, we study the calibration of uncertainty prediction for regression t
Externí odkaz:
https://doaj.org/article/80ab2db0392b46199b35cc1bf508c2de
Autor:
Zvika Ben-Haim, Zach Moshe, Moriah Royz, Yuval Levin, Nofar Peled Levi, Frederik Kratzert, Niv Giladi, Hila Noga, Dana Weitzner, Dafi Voloshin, Gregory Begelman, Gideon Dror, Sella Nevo, Shahar Timnat, Yotam Gigi, Liora Yuklea, Ofir Reich, Oren Gilon, Asher Metzger, Guy Shalev, Yossi Matias, Chen Barshai, Adi Gerzi Rosenthal, Vladimir Anisimov, Efrat Morin, Tal Shechter, Grey Nearing, Avinatan Hassidim, Ira Shavitt, Gal Elidan, Ronnie Maor
The operational flood forecasting system by Google was developed to provide accurate real-time flood warnings to agencies and the public, with a focus on riverine floods in large, gauged rivers. It became operational in 2018 and has since expanded ge
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::71a4149b2b83102b65c7ecaeeed7d573
https://doi.org/10.5194/hess-2021-554
https://doi.org/10.5194/hess-2021-554
Autor:
Sella Nevo, Efrat Morin, Adi Gerzi Rosenthal, Asher Metzger, Chen Barshai, Dana Weitzner, Dafi Voloshin, Frederik Kratzert, Gal Elidan, Gideon Dror, Gregory Begelman, Grey Nearing, Guy Shalev, Hila Noga, Ira Shavitt, Liora Yuklea, Moriah Royz, Niv Giladi, Nofar Peled Levi, Ofir Reich, Oren Gilon, Ronnie Maor, Shahar Timnat, Tal Shechter, Vladimir Anisimov, Yotam Gigi, Yuval Levin, Zach Moshe, Zvika Ben-Haim, Avinatan Hassidim, Yossi Matias
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8a9ffa6c756998128c621dc3244cd2e1
https://doi.org/10.5194/hess-2021-554-supplement
https://doi.org/10.5194/hess-2021-554-supplement
Publikováno v:
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
CVPR
CVPR
Large-batch SGD is important for scaling training of deep neural networks. However, without fine-tuning hyperparameter schedules, the generalization of the model may be hampered. We propose to use batch augmentation: replicating instances of samples
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dd7a331ad6ebeb0aafcf53d8ac914245
https://hdl.handle.net/20.500.11850/462535
https://hdl.handle.net/20.500.11850/462535