The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Nov. 28, 2023

Filed:

Mar. 07, 2018
Applicant:

International Business Machines Corporation, Armonk, NY (US);

Inventors:

Aaron K Baughman, Silver Spring, MD (US);

Stephen C. Hammer, Marietta, GA (US);

Micah Forster, Austin, TX (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06N 3/10 (2006.01); G06F 17/18 (2006.01); G06N 3/084 (2023.01); G06N 3/047 (2023.01); G06N 3/043 (2023.01);
U.S. Cl.
CPC ...
G06N 3/10 (2013.01); G06F 17/18 (2013.01); G06N 3/043 (2023.01); G06N 3/047 (2023.01); G06N 3/084 (2013.01);
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.


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