公开(公告)号：GB2584583A

公开(公告)日：2020-12-09

申请号：GB202015562

申请日：2019-03-06

Applicant: IBM

Inventor： AARON BAUGHMAN ,  STEPHEN HAMMER ,  MICAH FORSTER

IPC: G06N3/08 ,  G06N3/04

Abstract: Simulating uncertainty in an artificial neural network is provided. Aleatoric uncertainty is simulated to measure what the artificial neural network does not understand from sensor data received from an object operating in a real-world environment by adding random values to edge weights between nodes in the artificial neural network during backpropagation of output data of the artificial neural network and measuring impact on the output data by the added random values to the edge weights between the nodes. Epistemic uncertainty is simulated to measure what the artificial neural network does not know by dropping out a selected node from each respective layer of the artificial neural network during forward propagation of the sensor data and measuring impact of dropped out nodes on the output data of the artificial neural network. An action corresponding to the object is performed based on the impact of simulating the aleatoric and epistemic uncertainty.