Knowledge-based Residual Learning

被引：0

作者：

Zheng, Guanjie ^{[1
,2
]}

Liu, Chang ^{[1
]}

Wei, Hua ^{[2
]}

Jenkins, Porter ^{[2
]}

Chen, Chacha ^{[2
]}

Wen, Tao ^{[3
]}

Li, Zhenhui ^{[2
]}

机构：

[1] Shanghai Jiao Tong Univ, Shanghai, Peoples R China

[2] Penn State Univ, State Coll, PA 16801 USA

[3] Syracuse Univ, Syracuse, NY USA

来源：

PROCEEDINGS OF THE THIRTIETH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, IJCAI 2021 | 2021年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：1653 / 1659

页数：7

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