Hybrid learning for intelligent instruments

Abstract

Numerous instruments make inferences by fitting sensor data to knowledge that characterizes the physical world, where the knowledge result from human learning. The present intelligent instruments integrate machine learning elements with human learning elements so that they work in a hybrid fashion to improve the performance of inference making. In some embodiments human learning elements, e.g., scientific models, participate directly in the construction of some of the machine learning elements. In some embodiments machine learning elements help assess and drive the refinement of the inferences. The integration comprises training data synthesis, loss and gradient calculations, and autonomous adaptation.

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