2021
DOI: 10.1177/10943420211029302
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Co-design Center for Exascale Machine Learning Technologies (ExaLearn)

Abstract: Rapid growth in data, computational methods, and computing power is driving a remarkable revolution in what variously is termed machine learning (ML), statistical learning, computational learning, and artificial intelligence. In addition to highly visible successes in machine-based natural language translation, playing the game Go, and self-driving cars, these new technologies also have profound implications for computational and experimental science and engineering, as well as for the exascale computing syste… Show more

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Cited by 11 publications
(3 citation statements)
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“…The agent's goal is to select actions that maximize an encoded scientific goal. This reinforcement learning approach has been highly successful in game-playing [102,103], for instance, and shows promise for rapid decision-making in experimental contexts [16,41]. An alternative approach is to treat AE as an optimization problem in a multi-dimensional parameter space, which is defined as a set of signals-of-interest that vary over the range of control parameters.…”
Section: Autonomous Loopmentioning
confidence: 99%
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“…The agent's goal is to select actions that maximize an encoded scientific goal. This reinforcement learning approach has been highly successful in game-playing [102,103], for instance, and shows promise for rapid decision-making in experimental contexts [16,41]. An alternative approach is to treat AE as an optimization problem in a multi-dimensional parameter space, which is defined as a set of signals-of-interest that vary over the range of control parameters.…”
Section: Autonomous Loopmentioning
confidence: 99%
“…Alternatively, other decision-making modules should be considered when very high-speed decision making is required. Reinforcement learning methods that exploit neural network architectures can achieve the higher speed required for certain experiments [16].…”
Section: Modelingmentioning
confidence: 99%
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