Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Kai Krajsek"'
Autor:
Björn Hagemeier, Claudia Comito, Kai Krajsek, Achim Streit, Simon Hanselmann, Daniel Coquelin, Martin Siggel, Achim Basermann, Philipp Knechtges, Michael Tarnawa, Charlotte Debus, Markus Götz
Publikováno v:
IEEE BigData
IEEE 276-287 (2020). doi:10.1109/BigData50022.2020.9378050
2020 IEEE International Conference on Big Data (Big Data), Atlanta, GA, 2020-12-10-2020-12-13
IEEE 276-287 (2020). doi:10.1109/BigData50022.2020.9378050
2020 IEEE International Conference on Big Data (Big Data), Atlanta, GA, 2020-12-10-2020-12-13
To cope with the rapid growth in available data, the efficiency of data analysis and machine learning libraries has recently received increased attention. Although great advancements have been made in traditional array-based computations, most are li
Publikováno v:
Cham : Springer, Lecture Notes in Computer Science 12566, 78-92 (2020). doi:10.1007/978-3-030-64580-9_7
Machine Learning, Optimization, and Data Science
Machine Learning, Optimization, and Data ScienceThe Sixth International Conference on Machine Learning, Optimization, and Data Science, LOD2020, Siena, Italy, 2020-07-19-2020-07-22
Machine Learning, Optimization, and Data Science ISBN: 9783030645793
LOD (2)
Lecture Notes in Computer Science
Lecture Notes in Computer Science-Machine Learning, Optimization, and Data Science
Machine Learning, Optimization, and Data Science-6th International Conference, LOD 2020, Siena, Italy, July 19–23, 2020, Revised Selected Papers, Part II
Machine Learning, Optimization, and Data Science
Machine Learning, Optimization, and Data ScienceThe Sixth International Conference on Machine Learning, Optimization, and Data Science, LOD2020, Siena, Italy, 2020-07-19-2020-07-22
Machine Learning, Optimization, and Data Science ISBN: 9783030645793
LOD (2)
Lecture Notes in Computer Science
Lecture Notes in Computer Science-Machine Learning, Optimization, and Data Science
Machine Learning, Optimization, and Data Science-6th International Conference, LOD 2020, Siena, Italy, July 19–23, 2020, Revised Selected Papers, Part II
The successful training of deep neural networks is dependent on initialization schemes and choice of activation functions. Non-optimally chosen parameter settings lead to the known problem of exploding or vanishing gradients. This issue occurs when g
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f40159bae9de89e79ba405ed8c3b4030
https://juser.fz-juelich.de/record/889208
https://juser.fz-juelich.de/record/889208
Autor:
Constantini Samara, F. Lenartz, A. Di Gilio, C. Colombi, K. Maciejewska, Roberta Vecchi, Guido Pirovano, Kai Krajsek, Evangelia Diapouli, D. Mooibroek, Maria Grazia Perrone, K. Sega, Benjamin Golly, Daniele Contini, Fabiana Scotto, M. Masiol, Marcelo Pinho Almeida, E. Venturini, Giuseppe Calori, H.A.C. Denier van der Gon, Marta G. Vivanco, Daniela Cesari, Claudio A. Belis, Silvia Nava, G. Valli, Franco Lucarelli, Antoine Waked, Paolo Brotto, Véronique Riffault, Mihaela Mircea, Ettore Petralia, Eduardo Yubero, Jean-Luc Besombes, Jeroen Kuenen, M. Manousakas, Guillaume Siour, G. de Gennaro, A. Angyal, Jean-Luc Jaffrezo, Stig Hellebust, Petra Pokorná, M. Reizer, Fulvio Amato, Philip K. Hopke, Laurent Y. Alleman, Konstantinos Eleftheriadis, G. Argyropoulos, S. Bande, Paolo Prati, S. Pillon, Richard Kranenburg, Olivier Favez, Dikaia Saraga, Yahya Izadmanesh, Stefania Gilardoni, I. Beslic, Hendrik Elbern, Astrid Manders, Joana Ferreira, Romà Tauler, Stéphane Sauvage, P. Lazzeri, Mika Vestenius, Héctor Jorquera, D. Pernigotti, Lucyna Samek, Dalia Salameh, Marco Pandolfi, Marco Paglione, I. El Haddad, Martijn Schaap, A. Pietrodangelo, Maria Chiara Bove, D. Oliveira
Publikováno v:
Atmospheric environment: X
Atmospheric environment: X, 2020, 5, pp.100053. ⟨10.1016/j.aeaoa.2019.100053⟩
Atmospheric Environment: X, 5, 1-23
Atmospheric environment: X, Elsevier, 2020, 5, pp.100053. ⟨10.1016/j.aeaoa.2019.100053⟩
Atmospheric Environment: X, Vol 5, Iss, Pp-(2020)
Digital.CSIC. Repositorio Institucional del CSIC
instname
Atmospheric environment (1994) 5 (2020). doi:10.1016/j.aeaoa.2019.100053
info:cnr-pdr/source/autori:Belis C.A.; Pernigotti D.; Pirovano G.; Favez O.; Jaffrezo J.L.; Kuenen J.; Denier van Der Gon H.; Reizer M.; Riffault V.; Alleman L.Y.; Almeida M.; Amato F.; Angyal A.; Argyropoulos G.; Bande S.; Beslic I.; Besombes J.-L.; Bove M.C.; Brotto P.; Calori G.; Cesari D.; Colombi C.; Contini D.; De Gennaro G.; Di Gilio A.; Diapouli E.; El Haddad I.; Elbern H.; Eleftheriadis K.; Ferreira J.; Vivanco M.G.; Gilardoni S.; Golly B.; Hellebust S.; Hopke P.K.; Izadmanesh Y.; Jorquera H.; Krajsek K.; Kranenburg R.; Lazzeri P.; Lenartz F.; Lucarelli F.; Maciejewska K.; Manders A.; Manousakas M.; Masiol M.; Mircea M.; Mooibroek D.; Nava S.; Oliveira D.; Paglione M.; Pandolfi M.; Perrone M.; Petralia E.; Pietrodangelo A.; Pillon S.; Pokorna P.; Prati P.; Salameh D.; Samara C.; Samek L.; Saraga D.; Sauvage S.; Schaap M.; Scotto F.; Sega K.; Siour G.; Tauler R.; Valli G.; Vecchi R.; Venturini E.; Vestenius M.; Waked A.; Yubero E./titolo:Evaluation of receptor and chemical transport models for PM10 source apportionment/doi:10.1016%2Fj.aeaoa.2019.100053/rivista:Atmospheric environment (1994)/anno:2020/pagina_da:/pagina_a:/intervallo_pagine:/volume:5
Atmospheric environment: X, 2020, 5, pp.100053. ⟨10.1016/j.aeaoa.2019.100053⟩
Atmospheric Environment: X, 5, 1-23
Atmospheric environment: X, Elsevier, 2020, 5, pp.100053. ⟨10.1016/j.aeaoa.2019.100053⟩
Atmospheric Environment: X, Vol 5, Iss, Pp-(2020)
Digital.CSIC. Repositorio Institucional del CSIC
instname
Atmospheric environment (1994) 5 (2020). doi:10.1016/j.aeaoa.2019.100053
info:cnr-pdr/source/autori:Belis C.A.; Pernigotti D.; Pirovano G.; Favez O.; Jaffrezo J.L.; Kuenen J.; Denier van Der Gon H.; Reizer M.; Riffault V.; Alleman L.Y.; Almeida M.; Amato F.; Angyal A.; Argyropoulos G.; Bande S.; Beslic I.; Besombes J.-L.; Bove M.C.; Brotto P.; Calori G.; Cesari D.; Colombi C.; Contini D.; De Gennaro G.; Di Gilio A.; Diapouli E.; El Haddad I.; Elbern H.; Eleftheriadis K.; Ferreira J.; Vivanco M.G.; Gilardoni S.; Golly B.; Hellebust S.; Hopke P.K.; Izadmanesh Y.; Jorquera H.; Krajsek K.; Kranenburg R.; Lazzeri P.; Lenartz F.; Lucarelli F.; Maciejewska K.; Manders A.; Manousakas M.; Masiol M.; Mircea M.; Mooibroek D.; Nava S.; Oliveira D.; Paglione M.; Pandolfi M.; Perrone M.; Petralia E.; Pietrodangelo A.; Pillon S.; Pokorna P.; Prati P.; Salameh D.; Samara C.; Samek L.; Saraga D.; Sauvage S.; Schaap M.; Scotto F.; Sega K.; Siour G.; Tauler R.; Valli G.; Vecchi R.; Venturini E.; Vestenius M.; Waked A.; Yubero E./titolo:Evaluation of receptor and chemical transport models for PM10 source apportionment/doi:10.1016%2Fj.aeaoa.2019.100053/rivista:Atmospheric environment (1994)/anno:2020/pagina_da:/pagina_a:/intervallo_pagine:/volume:5
In this study, the performance of two types of source apportionment models was evaluated by assessing the results provided by 40 different groups in the framework of an intercomparison organised by FAIRMODE WG3 (Forum for air quality modelling in Eur
Publikováno v:
Computer Vision – ACCV 2016 ISBN: 9783319541808
ACCV (1)
ACCV (1)
We present an online algorithm for the efficient clustering of data drawn from a union of arbitrary dimensional, non-static subspaces. Our algorithm is based on an online min-Mahalanobis distance classifier, which simultaneously clusters and is updat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b9cf066f1aeeb21654bce93e17b7ba12
https://doi.org/10.1007/978-3-319-54181-5_23
https://doi.org/10.1007/978-3-319-54181-5_23
Autor:
Alvaro Valdebenito, Julius Vira, Athanasios Damialis, J. Parmentier, Matthieu Plu, Roberto Albertini, Oliver Gilles, Carmen Galán, Kai Krajsek, Marje Prank, Lennart Robertson, Joaquim Arteta, Sevcan Celenk, Arjo Segers, Michel Thibaudon, Despoina Vokou, Jordina Belmonte, Mikhail Sofiev, Ivana Hrga, John Douros, Rostislav Kouznetsov, Hendrik Elbern, Olga Ritenberga, Barbara Stepanovich, Birthe Marie Steensen, Maira Bonini, E. Friese
Publikováno v:
Atmospheric chemistry and physics / Discussions, 1-32 (2017). doi:10.5194/acp-2016-1189
A 6-models strong European ensemble of Copernicus Atmospheric Monitoring Service (CAMS) was run through the season of 2014 computing the olive pollen dispersion in Europe. The simulations have been compared with observations in 6 countries, members o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1346914d46dd89526961d3780be28a29
https://juser.fz-juelich.de/record/828163
https://juser.fz-juelich.de/record/828163
Publikováno v:
International Journal of Computer Vision. 80:375-405
We discuss the basic concepts of computer vision with stochastic partial differential equations (SPDEs). In typical approaches based on partial differential equations (PDEs), the end result in the best case is usually one value per pixel, the "expect
Autor:
Béatrice Josse, Muriel Joly, Christos Giannaros, S. Queguiner, Jonathan Guth, Hendrik Elbern, Matthias Beekmann, Johannes W. Kaiser, Adriana Coman, Kai Krajsek, E. Friese, Joaquim Arteta, Renske Timmermans, Anna Benedictow, R. van Versendaal, A. Drouin, Emanuele Emili, Alvaro Valdebenito, P. Moinat, Henk Eskes, D. Melas, A. Ung, Laurent Menut, Lennart Robertson, T. Morales, Julius Vira, Isabel M. Martínez, Natalia Liora, Arjo Segers, Martijn Schaap, N. Kadygrov, Jeroen Kuenen, Gilles Foret, E. Lopez, Richard Engelen, Vincent-Henri Peuch, Bertrand Bessagnet, Laure Malherbe, U. Kumar, L. Tarasson, Camilla Andersson, Anastasia Poupkou, F. Cheroux, J. Parmentier, R.L. Curier, Mikhail Sofiev, Laurence Rouil, Virginie Marécal, F. Meleux, Manu Anna Thomas, Michael Gauss, Matthieu Plu, S. Andersson, H.A.C. Denier van der Gon, P. F. J. van Velthoven, E. Jaumouille, A. Cansado, Andrea Piacentini, Robert Bergström, Augustin Colette
Publikováno v:
Geoscientific Model Development
Geoscientific Model Development, 2015, 8, pp.2777-2813. ⟨10.5194/gmd-8-2777-2015⟩
Geoscientific Model Development, European Geosciences Union, 2015, 8, pp.2777-2813. ⟨10.5194/gmd-8-2777-2015⟩
ARCIMIS. Archivo Climatológico y Meteorológico Institucional (AEMET)
Agencia Estatal de Meteorología (AEMET)
Geoscientific model development 8(9), 2777-2813 (2015). doi:10.5194/gmd-8-2777-2015
Geoscientific Model Development, Vol 8, Iss 9, Pp 2777-2813 (2015)
Geoscientific Model Development, 2015, 8, pp.2777-2813. ⟨10.5194/gmd-8-2777-2015⟩
Geoscientific Model Development, European Geosciences Union, 2015, 8, pp.2777-2813. ⟨10.5194/gmd-8-2777-2015⟩
ARCIMIS. Archivo Climatológico y Meteorológico Institucional (AEMET)
Agencia Estatal de Meteorología (AEMET)
Geoscientific model development 8(9), 2777-2813 (2015). doi:10.5194/gmd-8-2777-2015
Geoscientific Model Development, Vol 8, Iss 9, Pp 2777-2813 (2015)
This paper describes the pre-operational analysis and forecasting system developed during MACC (Monitoring Atmospheric Composition and Climate) and continued in the MACC-II (Monitoring Atmospheric Composition and Climate: Interim Implementation) Euro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7a278b2a229c1c464463f952434ea712
http://hdl.handle.net/11250/2382133
http://hdl.handle.net/11250/2382133
Autor:
Kai Krajsek
Publikováno v:
Proceedings of the 9th International Conference on Computer Vision Theory and Applications.
Autor:
Hanno Scharr, Kai Krajsek
Publikováno v:
CVPR
High-angular resolution diffusion imaging (HARDI) is a magnetic resonance technique estimating the direction of self-diffusion of water molecules in biological tissue. HARDI encodes at each pixel (voxel) the orientation distribution function (ODF) of
Autor:
Hanno Scharr, Kai Krajsek
Publikováno v:
AIP Conference Proceedings.
We present a framework for Bayesian estimation in kernel feature space with implicit statistical inference in a high or even infinite dimensional feature space. Like in kernel PCA, this space is related to the input space by a nonlinear map consistin