Temporal and Object Quantification Networks
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.
Cite
@article{arxiv.2106.05891,
title = {Temporal and Object Quantification Networks},
author = {Jiayuan Mao and Zhezheng Luo and Chuang Gan and Joshua B. Tenenbaum and Jiajun Wu and Leslie Pack Kaelbling and Tomer D. Ullman},
journal= {arXiv preprint arXiv:2106.05891},
year = {2021}
}
Comments
IJCAI 2021. First two authors contributed equally. Project page: http://toqnet.csail.mit.edu/