US 11,755,939 B2
Self-supervised self supervision by combining probabilistic logic with deep learning
Hoifung Poon, Bellevue, WA (US); and Hunter Lang, Bellevue, WA (US)
Assigned to MICROSOFT TECHNOLOGY LICENSING, LLC, Redmond, WA (US)
Filed by Microsoft Technology Licensing, LLC, Redmond, WA (US)
Filed on Jun. 24, 2020, as Appl. No. 16/911,097.
Prior Publication US 2021/0406741 A1, Dec. 30, 2021
Int. Cl. G06F 18/20 (2023.01); G06F 18/2323 (2023.01); G06N 7/01 (2023.01); G06F 18/22 (2023.01); G06F 18/214 (2023.01); G06F 18/2113 (2023.01)
CPC G06N 7/01 (2023.01) [G06F 18/214 (2023.01); G06F 18/2113 (2023.01); G06F 18/22 (2023.01)] 19 Claims
OG exemplary drawing
 
1. A method, comprising:
receiving output from a deep probabilistic logic module in response to running an initial set of virtual evidence through the deep probabilistic logic module;
using the output to automatically determine at least one factor as new virtual evidence for use with the deep probabilistic logic module based on a score for the at least one factor, wherein the at least one factor is a function of observed data;
applying a score function to generate the score for each factor in a set of factors to use with the deep probabilistic logic module, wherein the score function is a function of the output of the deep probabilistic logic module and a factor template to add to a factor graph of the deep probabilistic logic module; and
adding the new virtual evidence to the deep probabilistic logic module based on the score.