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Neuro-symbolic models combine deep learning and symbolic reasoning to produce better-performing hybrids. Not only do neuro-symbolic models perform better, but they also deal better with data scarcity, enable the direct incorporation of high-level domain knowledge, and are more explainable. However, these benefits come at the cost of increased complexity, which may deter the uninitiated from using these models. In this work, we present a framework to simplify the creation of neuro-symbolic models for tree crown delineation and tree species classification via the use of object-oriented programming and hyperparameter tuning algorithms. We show that models created using our framework outperform their non-neuro-symbolic counterparts by as much as two F1 points for crown delineation and three F1 points for species classification. Furthermore, our use of hyperparameter tuning algorithms allows users to experiment with multiple formulations of domain knowledge without the burden of manual tuning.
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Neuro-Causal Models
We describe a novel neuro-symbolic model architecture we term "neuro-causal models," that uses a synthesis of deep generative models and causal graphical models to automatically infer higher level symbolic information from lower level "raw features", while also allowing for rich relationships among the symbolic variables.
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- PAR ID:
- 10530102
- Editor(s):
- Hitzler, P; Sarker, MK; Eberhart, A
- Publisher / Repository:
- IOS Press
- Date Published:
- Journal Name:
- Frontiers in Artificial Intelligence and Applications
- Volume:
- 369
- ISSN:
- 978-1-64368-406-2
- Page Range / eLocation ID:
- 153-177
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- The DOI is not currently available.
