Opening the black box of neural nets: case studies in stop/top discrimination

24 Apr 2018 Thomas Roxlo Matthew Reece

We introduce techniques for exploring the functionality of a neural network and extracting simple, human-readable approximations to its performance. By performing gradient ascent on the input space of the network, we are able to produce large populations of artificial events which strongly excite a given classifier... (read more)

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