Abstract
This paper introduces the notion of the variadic neural network (VNN). The inputs to a variadic network are an arbitrary-length list of n-tuples of real numbers, where n is fixed. In contrast to a recurrent network which processes a list sequentially, typically being affected more by more recent list elements, a variadic network processes the list simultaneously and is affected equally by all list elements. Formally speaking, the network can be seen as instantiating a function on a multiset along with a member of that multiset. I describe a simple implementation of a variadic network architecture, the multi-layer variadic perceptron (MLVP), and present experimental results showing that such a network can learn various variadic functions by back-propagation.
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McGregor, S. (2007). Neural Network Processing for Multiset Data. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D. (eds) Artificial Neural Networks – ICANN 2007. ICANN 2007. Lecture Notes in Computer Science, vol 4668. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74690-4_47
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DOI: https://doi.org/10.1007/978-3-540-74690-4_47
Publisher Name: Springer, Berlin, Heidelberg
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