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Towards Grounding Conceptual Spaces in Neural Representations
June 15, 2017 Β· Declared Dead Β· π International Workshop on Neural-Symbolic Learning and Reasoning
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Authors
Lucas Bechberger, Kai-Uwe KΓΌhnberger
arXiv ID
1706.04825
Category
cs.AI: Artificial Intelligence
Citations
6
Venue
International Workshop on Neural-Symbolic Learning and Reasoning
Last Checked
4 months ago
Abstract
The highly influential framework of conceptual spaces provides a geometric way of representing knowledge. It aims at bridging the gap between symbolic and subsymbolic processing. Instances are represented by points in a high-dimensional space and concepts are represented by convex regions in this space. In this paper, we present our approach towards grounding the dimensions of a conceptual space in latent spaces learned by an InfoGAN from unlabeled data.
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