Robustness of tensor product networks using distributed representations

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Abstract
This paper describes experiments on on the robustness of tensor product networks using distributed representations, for recall tasks. The results of the experiments indicate, among other things, that the degree of robustness increases with the number of binding units and decreases with the fraction of the space of possible facts that have been taught to the network. Mean recall scores decrease linearly with the proportion of binding units inactivated, and recall score variance depends linearly on number of binding units and on number of facts taught to the network.
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Author(s)
Wilson, William Hulme
Halford, Graeme S
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Publication Year
1998
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Conference Paper
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UNSW Faculty
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download peer-reviewed preprint.pdf 38.25 KB Adobe Portable Document Format
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