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Surrogate Neural Networks to Estimate Parametric Sensitivity of Ocean Models., , , , , , , and . CoRR, (2023)DeepAstroUDA: semi-supervised universal domain adaptation for cross-survey galaxy morphology classification and anomaly detection., , , , , , and . Mach. Learn. Sci. Technol., 4 (2): 25013 (June 2023)A Taxonomy of Error Sources in HPC I/O Machine Learning Models., , , , , , , , and . SC, page 16:1-16:14. IEEE, (2022)Modeling I/O Performance Variability Using Conditional Variational Autoencoders., , , , , , and . CLUSTER, page 109-113. IEEE Computer Society, (2018)Memristor-Spikelearn: A Spiking Neural Network Simulator for Studying Synaptic Plasticity under Realistic Device and Circuit Behaviors., , , and . DATE, page 1-6. IEEE, (2023)Toward Generalizable Models of I/O Throughput., , , , , , and . ROSS@SC, page 41-49. IEEE, (2020)HPC I/O throughput bottleneck analysis with explainable local models., , , , , , and . SC, page 33. IEEE/ACM, (2020)Improving Scalability of Parallel CNN Training by Adjusting Mini-Batch Size at Run-Time., , , , , , , and . IEEE BigData, page 830-839. IEEE, (2019)Analysis and Correlation of Application I/O Performance and System-Wide I/O Activity., , , , , , and . NAS, page 1-10. IEEE Computer Society, (2017)A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems., , , , , , , , , and 37 other author(s). CoRR, (2023)