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Workflow Anomaly Detection with Graph Neural Networks., , , , , , , , , and . WORKS@SC, page 35-42. IEEE, (2022)Formalizing the Generalization-Forgetting Trade-off in Continual Learning., and . NeurIPS, page 17284-17297. (2021)Deep learning inspired prognostics scheme for applications generating big data., , and . IJCNN, page 3296-3302. IEEE, (2017)A Multi-Step Nonlinear Dimension-Reduction Approach with Applications to Big Data., , and . IEEE Trans. Knowl. Data Eng., 31 (12): 2249-2261 (2019)Direct Error Driven Learning for Deep Neural Networks with Applications to Bigdata., , and . INNS Conference on Big Data, volume 144 of Procedia Computer Science, page 89-95. Elsevier, (2018)Autonomy Loops for Monitoring, Operational Data Analytics, Feedback, and Response in HPC Operations., , , , , , , , , and 17 other author(s). CLUSTER Workshops, page 37-43. IEEE, (2023)Automated Continual Learning of Defect Identification in Coherent Diffraction Imaging., , , , , , , and . AI4S, page 1-6. IEEE, (2022)Self-supervised Learning for Anomaly Detection in Computational Workflows., , , , , , and . CoRR, (2023)Flow-Bench: A Dataset for Computational Workflow Anomaly Detection., , , , , , and . CoRR, (2023)Hierarchical Mahalanobis Distance Clustering Based Technique for Prognostics in Applications Generating Big Data., and . SSCI, page 516-521. IEEE, (2015)