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Neural Logic Machines

Resource type
Authors/contributors
Title
Neural Logic Machines
Abstract
We propose the Neural Logic Machine (NLM), a neural-symbolic architecture for both inductive learning and logic reasoning. NLMs exploit the power of both neural networks---as function approximators, and logic programming---as a symbolic processor for objects with properties, relations, logic connectives, and quantifiers. After being trained on small-scale tasks (such as sorting short arrays), NLMs can recover lifted rules, and generalize to large-scale tasks (such as sorting longer arrays). In our experiments, NLMs achieve perfect generalization in a number of tasks, from relational reasoning tasks on the family tree and general graphs, to decision making tasks including sorting arrays, finding shortest paths, and playing the blocks world. Most of these tasks are hard to accomplish for neural networks or inductive logic programming alone.
Publication
arXiv:1904.11694 [cs, stat]
Date
2019-04-26
Accessed
2019-11-24T16:33:13Z
Library Catalog
Extra
ZSCC: 0000008 arXiv: 1904.11694
Notes

Comment: ICLR 2019. Project page: https://sites.google.com/view/neural-logic-machines

Citation
Dong, H., Mao, J., Lin, T., Wang, C., Li, L., & Zhou, D. (2019). Neural Logic Machines. ArXiv:1904.11694 [Cs, Stat]. Retrieved from http://arxiv.org/abs/1904.11694
MACHINE LEARNING
MODEL CHECKING AND STATE MACHINES
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