Scientific machine learning benchmarks

The breakthrough in Deep Learning neural networks has transformed the use of AI and machine learning technologies for the analysis of very large experimental datasets. These datasets are typically generated by large-scale experimental facilities at national laboratories. In the context of science, scientific machine learning focuses on training machines to identify patterns, trends, and anomalies to extract meaningful scientific insights from such datasets. With a new generation of experimental facilities, the rate of data generation and the scale of data volumes will increasingly require the use of more automated data analysis.

[1]  Torsten Hoefler,et al.  A Modular Benchmarking Infrastructure for High-Performance and Reproducible Deep Learning , 2019, 2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS).

[2]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[3]  Fangfang Xia,et al.  Performance, Energy, and Scalability Analysis and Improvement of Parallel Cancer Deep Learning CANDLE Benchmarks , 2019, ICPP.

[4]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[5]  J. A. Hartigan,et al.  A k-means clustering algorithm , 1979 .

[6]  Paris Perdikaris,et al.  Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations , 2019, J. Comput. Phys..

[7]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[8]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[9]  Habib N. Najm,et al.  Workshop Report on Basic Research Needs for Scientific Machine Learning: Core Technologies for Artificial Intelligence , 2018 .

[10]  Pierre Baldi,et al.  Autoencoders, Unsupervised Learning, and Deep Architectures , 2011, ICML Unsupervised and Transfer Learning.

[11]  Andrew J. Davison,et al.  RLBench: The Robot Learning Benchmark & Learning Environment , 2019, IEEE Robotics and Automation Letters.

[12]  Jason Yosinski,et al.  Hamiltonian Neural Networks , 2019, NeurIPS.

[13]  Kaivalya M. Dixit,et al.  The SPEC benchmarks , 1991, Parallel Comput..

[14]  Terrence J. Sejnowski,et al.  The Deep Learning Revolution , 2018 .

[15]  Ewen Callaway,et al.  ‘It will change everything’: DeepMind’s AI makes gigantic leap in solving protein structures , 2020, Nature.

[16]  O. Lahav,et al.  Benchmarking and scalability of machine-learning methods for photometric redshift estimation , 2021, Monthly Notices of the Royal Astronomical Society.

[17]  T. Perring,et al.  Interpretable, calibrated neural networks for analysis and understanding of inelastic neutron scattering data , 2020, Journal of physics. Condensed matter : an Institute of Physics journal.

[18]  Jia Deng,et al.  A large-scale hierarchical image database , 2009, CVPR 2009.

[19]  David H. Bailey,et al.  The Nas Parallel Benchmarks , 1991, Int. J. High Perform. Comput. Appl..

[20]  Stefanos Kaxiras,et al.  Splash-3: A properly synchronized benchmark suite for contemporary research , 2016, 2016 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS).

[21]  Kunle Olukotun,et al.  DAWNBench : An End-to-End Deep Learning Benchmark and Competition , 2017 .

[22]  Jack J. Dongarra,et al.  HPC Challenge Benchmark , 2011, Encyclopedia of Parallel Computing.