Knowledge-based Residual Learning

Guanjie Zheng, Chang Liu, Hua Wei, Porter Jenkins, Chacha Chen, Tao Wen, Zhenhui Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Small data has been a barrier for many machine learning tasks, especially when applied in scientific domains. Fortunately, we can utilize domain knowledge to make up the lack of data. Hence, in this paper, we propose a hybrid model KRL that treats domain knowledge model as a weak learner and uses another neural net model to boost it. We prove that KRL is guaranteed to improve over pure domain knowledge model and pure neural net model under certain loss functions. Extensive experiments have shown the superior performance of KRL over baselines. In addition, several case studies have explained how the domain knowledge can assist the prediction.

Original languageEnglish (US)
Title of host publicationProceedings of the 30th International Joint Conference on Artificial Intelligence, IJCAI 2021
EditorsZhi-Hua Zhou
PublisherInternational Joint Conferences on Artificial Intelligence
Pages1653-1659
Number of pages7
ISBN (Electronic)9780999241196
StatePublished - 2021
Event30th International Joint Conference on Artificial Intelligence, IJCAI 2021 - Virtual, Online, Canada
Duration: Aug 19 2021Aug 27 2021

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
ISSN (Print)1045-0823

Conference

Conference30th International Joint Conference on Artificial Intelligence, IJCAI 2021
Country/TerritoryCanada
CityVirtual, Online
Period8/19/218/27/21

ASJC Scopus subject areas

  • Artificial Intelligence

Fingerprint

Dive into the research topics of 'Knowledge-based Residual Learning'. Together they form a unique fingerprint.

Cite this