Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Sella Nevo"'
Autor:
Oleg Zlydenko, Gal Elidan, Avinatan Hassidim, Doron Kukliansky, Yossi Matias, Brendan Meade, Alexandra Molchanov, Sella Nevo, Yohai Bar-Sinai
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-8 (2023)
Abstract Forecasting the timing of earthquakes is a long-standing challenge. Moreover, it is still debated how to formulate this problem in a useful manner, or to compare the predictive power of different models. Here, we develop a versatile neural e
Externí odkaz:
https://doaj.org/article/b3e7bab22e9a4e088445a96c315acaa3
Autor:
Frederik Kratzert, Grey Nearing, Nans Addor, Tyler Erickson, Martin Gauch, Oren Gilon, Lukas Gudmundsson, Avinatan Hassidim, Daniel Klotz, Sella Nevo, Guy Shalev, Yossi Matias
Publikováno v:
Scientific Data, Vol 10, Iss 1, Pp 1-11 (2023)
Abstract High-quality datasets are essential to support hydrological science and modeling. Several CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) datasets exist for specific countries or regions, however these datasets lack st
Externí odkaz:
https://doaj.org/article/5b1436c4feef4818b712c766b37627f2
Autor:
Grey S. Nearing, Daniel Klotz, Jonathan M. Frame, Martin Gauch, Oren Gilon, Frederik Kratzert, Alden Keefe Sampson, Guy Shalev, Sella Nevo
Publikováno v:
Hydrology and Earth System Sciences. 26:5493-5513
Ingesting near-real-time observation data is a critical component of many operational hydrological forecasting systems. In this paper, we compare two strategies for ingesting near-real-time streamflow observations into long short-term memory (LSTM) r
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b373ba75c35030ff10e1f7ce1d33c2f7
https://doi.org/10.5194/hess-26-5493-2022
https://doi.org/10.5194/hess-26-5493-2022
Autor:
R Lily Hu, Damien Pierce, Yusef Shafi, Anudhyan Boral, Vladimir Anisimov, Sella Nevo, Yi-fan Chen
Publikováno v:
The International Journal of High Performance Computing Applications. 36:510-523
Recent advancements in hardware accelerators such as Tensor Processing Units (TPUs) speed up computation time relative to Central Processing Units (CPUs) not only for machine learning but, as demonstrated here, also for scientific modeling and comput
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f6d0512bb50ce31ace14d47e9d13f481
https://doi.org/10.1177/10943420221102873
https://doi.org/10.1177/10943420221102873
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
Autor:
J. M. Frame, Martin Gauch, Alden Keefe Sampson, Guy Shalev, Daniel Klotz, Sella Nevo, Frederik Kratzert, Grey Nearing
Ingesting near-real-time observation data is a critical component of many operational hydrological forecasting systems. In this paper we compare two strategies for ingesting near-real-time streamflow observations into Long Short-Term Memory (LSTM) ra
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2637c6e7320fbad8e11d43aa06efc326
https://hess.copernicus.org/preprints/hess-2021-515/
https://hess.copernicus.org/preprints/hess-2021-515/
Publikováno v:
SAM
We consider multiple matrix regression tasks that share common weights in order to reduce sample complexity. For this purpose, we introduce the common mechanism regression model which assumes a shared right low-rank component across all tasks, but al
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7798133e3675a2fb5d38181b1dc69892
https://doi.org/10.1109/sam48682.2020.9104279
https://doi.org/10.1109/sam48682.2020.9104279
Autor:
Sella Nevo, Ran El-Yaniv, Frederik Kratzert, Zach Moshe, Efrat Morin, Gal Elidan, Asher Metzger
Accurate and scalable hydrologic models are essential building blocks of several important applications, from water resource management to timely flood warnings. In this work we present a novel family of hydrologic models, called HydroNets, that leve
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::70ed967132837e8eea2c13ccb85b057a
https://doi.org/10.5194/egusphere-egu2020-4135
https://doi.org/10.5194/egusphere-egu2020-4135
Autor:
Efrat Morin, Sella Nevo, Asher Metzger, Vova Anisimov, Gal Elidan, Zach Moshe, Ran El-Yaniv, Zvika Ben-Haim, Ofir Reich, Guy Shalev
One of the major natural disasters is flooding, which causes thousands of fatalities, affects the lives of hundreds of millions, and results in huge economic damages annually. Google’s Flood Forecasting Initiative aims at providing high-resolution
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bb4fd030ab7acd272a17f7e4af430f1b
https://doi.org/10.5194/egusphere-egu2020-4134
https://doi.org/10.5194/egusphere-egu2020-4134