Corrigendum: Applications and techniques for fast machine learning in science

[This corrects the article DOI: 10.3389/fdata.2022.787421.].

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Bibliographic Details
Published in:Frontiers in big data 2023-10, Vol.6, p.1301942-1301942
Main Authors: 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.
Format: Article
Language:English
Subjects:
Big Data
codesign
coprocessors
heterogeneous computing
machine learning for science
particle physics
Online Access:Get full text
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Description
Summary:[This corrects the article DOI: 10.3389/fdata.2022.787421.].
ISSN:2624-909X
2624-909X
DOI:10.3389/fdata.2023.1301942