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Roadmap on fast machine learning for science

delete2026-03-17
delete0
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OA
AI
S
Sioni Summers *
A
Alex Tapper *
T
Thea Klaeboe Aarrestad
C
Chen Qin
K
Karin Rathsman
M
M. J. V. Streeter
C
Charlotte A.J. Palmer
J
Jonathan Citrin
C
Changgang Zheng
N
Noa Zilberman
A
Alexander Titterton
T
Tobias Becker
DOI：10.1088/2632-2153/ae484bdelete
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摘要

摘要

En 中文
近年来，对粒子物理实验中微秒级机器学习（ML）推理的需求已显现出来，特别是在欧洲核子研究中心（CERN）大型强子对撞机（LHC）实验即将进行的升级方面。围绕开发所需定制硬件平台和工具的需求，一个社群已经形成。本报告呈现的内容源自“科学领域快速机器学习”社群举办的最新研讨会，包含了一系列关于不同科学领域中快速机器学习现状及其支持技术的观点集合。
Keyword:
fast machine learning
particle physics
Large Hadron Collider
ML inference
scientific computing
AI总结

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期刊

M
machine learning: science and technology
IF:
0
论文数:
116
被引数:
0

机构

G
Google DeepMind
学者数:
73
论文数: 19
被引数: 0
Q
queen's university belfast
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1.0K
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I
imperial college london
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9.9K
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G
groq
学者数:
1
论文数: 1
被引数: 0
E
European Spallation Source ERIC
学者数:
4
论文数: 3
被引数: 0
E
eth zurich
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2.3K
论文数: 1.1K
被引数: 0
U
university of oxford
学者数:
9.8W
论文数: 8.6W
被引数: 137
G
graphcore
学者数:
2
论文数: 2
被引数: 0
C
CERN
学者数:
1.1K
论文数: 116
被引数: 7
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