In:
Frontiers in Big Data, Frontiers Media SA, Vol. 5 ( 2022-4-12)
Abstract:
In this community review report, we discuss applications and techniques for fast machine learning (ML) in science—the concept of integrating powerful ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms. We also present overlapping challenges across the multiple scientific domains where common solutions can be found. This community report is intended to give plenty of examples and inspiration for scientific discovery through integrated and accelerated ML solutions. This is followed by a high-level overview and organization of technical advances, including an abundance of pointers to source material, which can enable these breakthroughs.
Type of Medium:
Online Resource
ISSN:
2624-909X
DOI:
10.3389/fdata.2022.787421
DOI:
10.3389/fdata.2022.787421.s001
DOI:
10.3389/fdata.2022.787421.s002
DOI:
10.3389/fdata.2022.787421.s003
DOI:
10.3389/fdata.2022.787421.s004
DOI:
10.3389/fdata.2022.787421.s005
DOI:
10.3389/fdata.2022.787421.s006
DOI:
10.3389/fdata.2022.787421.s007
DOI:
10.3389/fdata.2022.787421.s008
DOI:
10.3389/fdata.2022.787421.s009
DOI:
10.3389/fdata.2022.787421.s010
DOI:
10.3389/fdata.2022.787421.s011
DOI:
10.3389/fdata.2022.787421.s012
Language:
Unknown
Publisher:
Frontiers Media SA
Publication Date:
2022
detail.hit.zdb_id:
2957497-3