Massachusetts Institute of Technology

Luke Skelly

Jason R Thornton

Michael T Chan

Ronald Duarte

Daniel J Scarafoni

Systems and methods are provided for selecting an optimized data model architecture subject to resource constraints. One or more resource constraints for target deployment are identified, and random model architectures are generated from a set of model architecture production rules subject to the one or more resource constraints. Each random model architecture is defined by randomly chosen values for one or more meta parameters and one or more layer parameters. One or more of the random model architectures are adaptively refined to improve performance relative to a metric, and the refined model architecture with the best performance relative to the metric is selected.

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Groton, MA, United States of America

Luke Skelly