Abstract
An analytic process is iterative between two agents, an analyst and an analytic
toolbox. Each iteration comprises three main steps: preparing a dataset, running
an analytic tool, and evaluating the result, where dataset preparation and
result evaluation, conducted by the analyst, are largely domain-knowledge
driven. In this work, the focus is on automating the result evaluation step.
The underlying problem is to identify plots that are deemed
interesting by an analyst. We propose a methodology to learn such analyst’s
intent based on Generative Adversarial Networks (GANs) and demonstrate its
applications in the context of production yield optimization using
data collected from several product lines.