{"type":"ScholarlyArticle","title":"Temporal and Object Quantification Networks","authors":[{"name":"Jiayuan Mao","slug":"jiayuan-mao","given":"Jiayuan","family":"Mao","affiliation":""},{"name":"Zhezheng Luo","slug":"zhezheng-luo","given":"Zhezheng","family":"Luo","affiliation":""},{"name":"Chuang Gan","slug":"chuang-gan","given":"Chuang","family":"Gan","affiliation":""},{"name":"Joshua B. Tenenbaum","slug":"joshua-b-tenenbaum","given":"Joshua B.","family":"Tenenbaum","affiliation":""},{"name":"Jiajun Wu","slug":"jiajun-wu","given":"Jiajun","family":"Wu","affiliation":""},{"name":"Leslie Pack Kaelbling","slug":"leslie-pack-kaelbling","given":"Leslie Pack","family":"Kaelbling","affiliation":""},{"name":"Tomer D. Ullman","slug":"tomer-d-ullman","given":"Tomer D.","family":"Ullman","affiliation":""}],"abstract":"We present Temporal and Object Quantification Networks (TOQ-Nets), a new class of neuro-symbolic networks with a structural bias that enables them to learn to recognize complex relational-temporal events. This is done by including reasoning layers that implement finite-domain quantification over objects and time. The structure allows them to generalize directly to input instances with varying numbers of objects in temporal sequences of varying lengths. We evaluate TOQ-Nets on input domains that require recognizing event-types in terms of complex temporal relational patterns. We demonstrate that TOQ-Nets can generalize from small amounts of data to scenarios containing more objects than were present during training and to temporal warpings of input sequences.","doi":"10.24963/ijcai.2021/386","url":"https://ijcai.org/proceedings/2021/386","pdf":"https://ijcai.org/proceedings/2021/0386.pdf","markdown":"https://ijcai.org/proceedings/2021/386.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/386","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"Machine Learning","track":"Main Track","number":386,"pages":{"first":2804,"last":2811},"keywords":["Machine Learning: Relational Learning","Machine Learning: Neuro-Symbolic Methods"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}