Temporal and Object Quantification Networks
Name
2021IJCAI-TOQNet.pdf
Description
Accepted version
Size
1.65 MB
Format
Adobe PDF
Checksum (MD5)
a86273bce2b4584007788f64b040edeb
Author(s)
Mao, Jiayuan
Luo, Zhezheng
Gan, Chuang
Tenenbaum, Joshua B
Wu, Jiajun
Kaelbling, Leslie Pack
Ullman, Tomer D
Date Issued
2021
Journal
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence
Publisher
International Joint Conferences on Artificial Intelligence
Citation
Mao, Jiayuan, Luo, Zhezheng, Gan, Chuang, Tenenbaum, Joshua B, Wu, Jiajun et al. 2021. "Temporal and Object Quantification Networks." Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence.
Version
Author's final manuscript
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.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
MIT-IBM Watson AI Lab
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.24963/IJCAI.2021/386
