Fair Isaac Corporation
Density based confidence measures of neural networks for reliable predictions

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Abstract:

A system and method for learning and associating reliability and confidence corresponding to a model's predictions by examining the support associated with datapoints in the variable phase space in terms of data coverage, and their impact on the weights distribution. The approach disclosed herein examines the impact of minor perturbations on a small fraction of the training exemplars in the variable phase space on the weights to understand whether the weights remain unperturbed or change significantly.

Status:
Grant
Type:

Utility

Filling date:

25 Feb 2019

Issue date:

11 May 2021