%0 Conference Paper %B Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence %D 2021 %T Temporal and Object Quantification Networks %A Mao, Jiayuan %A Luo, Zhezheng %A Gan, Chuang %A Joshua B. Tenenbaum %A Wu, Jiajun %A Kaelbling, Leslie Pack %A Ullman, Tomer D. %E Zhou, Zhi-Hua %Y Gini, Maria %X

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.

%B Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence %C Montreal, Canada %8 06/2021 %G eng %U https://www.ijcai.org/proceedings/2021 %R 10.24963/ijcai.2021/386