@conference {5063,
	title = {Temporal and Object Quantification Networks},
	booktitle = {Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence},
	year = {2021},
	month = {06/2021},
	address = {Montreal, Canada},
	abstract = {<p>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.</p>
},
	doi = {10.24963/ijcai.2021/386},
	url = {https://www.ijcai.org/proceedings/2021},
	author = {Mao, Jiayuan and Luo, Zhezheng and Gan, Chuang and Joshua B. Tenenbaum and Wu, Jiajun and Kaelbling, Leslie Pack and Ullman, Tomer D.},
	editor = {Zhou, Zhi-Hua}
}
