Uncertainty scoring for neural networks via stochastic weight perturbations

A computer-implemented method, comprising:

• accessing, by a device operatively coupled to a processor, a trained neural network and a data candidate on which the trained neural network is to be executed;
• and
• generating, by the device, an uncertainty indicator representing how confidently executable or how unconfidently executable the trained neural network is with respect to the data candidate, based on a set of perturbed instantiations of the trained neural network.

A computer program product for facilitating improved uncertainty scoring for neural networks via stochastic weight perturbations, the computer program product comprising a computer-readable memory having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to:

• access a trained neural network and a data candidate on which the trained neural network is to be executed;
• and
• generate an uncertainty indicator representing how confidently executable or how unconfidently executable the trained neural network is with respect to the data candidate, based on a set of perturbed instantiations of the trained neural network.