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On the Need for Reproducible Numerical Accuracy through Intelligent Runtime Selection of Reduction Algorithms at the Extreme Scale.

, , and . CLUSTER, page 166-175. IEEE Computer Society, (2015)

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Cospectral mates for the union of some classes in the Johnson association scheme., , and . CoRR, (2017)Building High-Throughput Neural Architecture Search Workflows via a Decoupled Fitness Prediction Engine., , , and . IEEE Trans. Parallel Distributed Syst., 33 (11): 2913-2926 (2022)Learning to Predict Material Structure from Neutron Scattering Data., , , , , and . IEEE BigData, page 4490-4497. IEEE, (2019)PEng4NN: An Accurate Performance Estimation Engine for Efficient Automated Neural Network Architecture Search., , , , and . CoRR, (2021)Structure Prediction from Neutron Scattering Profiles: A Data Sciences Approach., , , , and . IEEE BigData, page 1147-1155. IEEE, (2020)A Novel Pruning Method for Convolutional Neural Networks Based off Identifying Critical Filters., and . PEARC, page 63:1-63:7. ACM, (2019)Multi-Objective Optimization for Size and Resilience of Spiking Neural Networks., , , , and . CoRR, (2020)Creating a portable, high-level graph analytics paradigm for compute and data-intensive applications., , , , and . IJHPCN, 13 (1): 105-118 (2019)Co-design Center for Exascale Machine Learning Technologies (ExaLearn)., , , , , , , , , and 37 other author(s). Int. J. High Perform. Comput. Appl., 35 (6): 598-616 (2021)Multi-Objective Optimization for Size and Resilience of Spiking Neural Networks., , , , and . UEMCON, page 433-439. IEEE, (2019)