The status of Sigma’s grounding in graphical models is challenged by the ways in which their semantics has been violated while incorporating rule-based reasoning into them. This has led to a rethinking of what goes on in its graphical architecture, with results that include a straightforward extension to feedforward neural networks (although not yet with learning).
CITATION STYLE
Rosenbloom, P. S., Demski, A., & Ustun, V. (2016). Rethinking sigma’s graphical architecture: An extension to neural networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9782, pp. 84–94). Springer Verlag. https://doi.org/10.1007/978-3-319-41649-6_9
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