Applications and Techniques for Fast Machine Learning in Science.
Applications and Techniques for Fast Machine Learning in Science.
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DOI：
10.3389/fdata.2022.787421
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发表时间：
2022
影响因子：
3.1
通讯作者：
Warburton, Thomas K.
中科院分区：
文献类型：
--
作者：
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;Baehr, Steffen;Becker, Juergen;Berthold, Anne-Sophie;Bonventre, Richard J.;Bravo, Tomas E. Muller;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.
关键词：
在这份社区评论报告中，我们讨论了快速机器学习（ML）在科学中的应用和技术-将强大的ML方法集成到实时实验数据处理循环中以加速科学发现的概念。该报告的材料建立在Fast ML for Science社区举办的两个研讨会的基础上，涵盖了三个主要领域：快速ML在许多科学领域的应用;培训和实施高性能和资源高效的ML算法的技术;以及部署这些算法的计算架构，平台和技术。我们还提出了跨多个科学领域的重叠挑战，在这些领域可以找到共同的解决方案。这份社区报告旨在通过集成和加速ML解决方案为科学发现提供大量示例和灵感。其次是对技术进步的高层次概述和组织，包括对源材料的丰富指示，这些材料可以实现这些突破。
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.
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影响因子：
8.6
作者：
Abbott, B. P.;Abbott, R.;Zweizig, J.
通讯作者：
Zweizig, J.
影响因子：
3.5
作者：
Affeldt, C.;Danzmann, K.;Wittel, H.
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Wittel, H.
影响因子：
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Zhao, X.
影响因子：
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通讯作者：
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影响因子：
6.5
作者：
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通讯作者：
Zonca, A.