{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T14:11:35Z","timestamp":1785420695609,"version":"3.56.0"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,8]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.24963\/ijcai.2021\/228","type":"proceedings-article","created":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T07:00:49Z","timestamp":1628665249000},"page":"1653-1659","source":"Crossref","is-referenced-by-count":13,"title":["Knowledge-based Residual Learning"],"prefix":"10.24963","author":[{"given":"Guanjie","family":"Zheng","sequence":"first","affiliation":[{"name":"Shanghai Jiao Tong University"},{"name":"The Pennsylvania State University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chang","family":"Liu","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hua","family":"Wei","sequence":"additional","affiliation":[{"name":"The Pennsylvania State University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Porter","family":"Jenkins","sequence":"additional","affiliation":[{"name":"The Pennsylvania State University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chacha","family":"Chen","sequence":"additional","affiliation":[{"name":"The Pennsylvania State University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Wen","sequence":"additional","affiliation":[{"name":"Syracuse University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenhui","family":"Li","sequence":"additional","affiliation":[{"name":"The Pennsylvania State University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"10584","event":{"name":"Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}","theme":"Artificial Intelligence","location":"Montreal, Canada","acronym":"IJCAI-2021","number":"30","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2021,8,19]]},"end":{"date-parts":[[2021,8,27]]}},"container-title":["Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T07:02:07Z","timestamp":1628665327000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2021\/228"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2021,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2021\/228","relation":{},"subject":[],"published":{"date-parts":[[2021,8]]}}}