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Modeling I/O Performance Variability Using Conditional Variational Autoencoders.

, , , , , , and . CLUSTER, page 109-113. IEEE Computer Society, (2018)

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Active-learning-based surrogate models for empirical performance tuning., , and . CLUSTER, page 1-8. IEEE Computer Society, (2013)Poster: An Exascale Workload Study., , , , , , , , and . SC Companion, page 1465. IEEE Computer Society, (2012)Exploiting Performance Portability in Search Algorithms for Autotuning., , , and . IPDPS Workshops, page 1535-1544. IEEE Computer Society, (2016)DeepHyper: Asynchronous Hyperparameter Search for Deep Neural Networks., , , , and . HiPC, page 42-51. IEEE, (2018)Neuromorphic Architecture Optimization for Task-Specific Dynamic Learning., , and . ICONS, page 5:1-5:5. ACM, (2019)Value-Added Chemical Discovery Using Reinforcement Learning., , , , and . CoRR, (2019)Incremental Local Search in Ant Colony Optimization: Why It Fails for the Quadratic Assignment Problem., , , and . ANTS Workshop, volume 4150 of Lecture Notes in Computer Science, page 156-166. Springer, (2006)Estimation-Based Local Search for Stochastic Combinatorial Optimization Using Delta Evaluations: A Case Study on the Probabilistic Traveling Salesman Problem., , , and . INFORMS J. Comput., 20 (4): 644-658 (2008)Energy-performance tradeoffs in multilevel checkpoint strategies., , , , , and . CLUSTER, page 278-279. IEEE Computer Society, (2014)Abstract: An Exascale Workload Study., , , , , , , , and . SC Companion, page 1463-1464. IEEE Computer Society, (2012)