Corrigendum: Applications and techniques for fast machine learning in science

Detalles Bibliográficos
Autores principales: Deiana, Allison McCarn, Tran, Nhan, Agar, Joshua, Blott, Michaela, Di Guglielmo, Giuseppe, Duarte, Javier, Harris, Philip, Hauck, Scott, Liu, Mia, Neubauer, Mark S., Ngadiuba, Jennifer, Ogrenci-Memik, Seda, Pierini, Maurizio, Aarrestad, Thea, Bähr, Steffen, Becker, Jürgen, Berthold, Anne-Sophie, Bonventre, Richard J., Müller Bravo, Tomás E., Diefenthaler, Markus, Dong, Zhen, Fritzsche, Nick, Gholami, Amir, Govorkova, Ekaterina, Guo, Dongning, Hazelwood, Kyle J., Herwig, Christian, Khan, Babar, Kim, Sehoon, Klijnsma, Thomas, Liu, Yaling, Lo, Kin Ho, Nguyen, Tri, Pezzullo, Gianantonio, Rasoulinezhad, Seyedramin, Rivera, Ryan A., Scholberg, Kate, Selig, Justin, Sen, Sougata, Strukov, Dmitri, Tang, William, Thais, Savannah, Unger, Kai Lukas, Vilalta, Ricardo, von Krosigk, Belina, Wang, Shen, Warburton, Thomas K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Big Data
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10614289/
https://www.ncbi.nlm.nih.gov/pubmed/37908454
http://dx.doi.org/10.3389/fdata.2023.1301942

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