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Representing Unordered Data Using Complex-Weighted Multiset Automata

Justin DeBenedetto · David Chiang

Keywords: [ Architectures ] [ Deep Sequence Models ] [ Matrix/Tensor Methods ] [ General Machine Learning Techniques ]


Unordered, variable-sized inputs arise in many settings across multiple fields. The ability for set- and multiset-oriented neural networks to handle this type of input has been the focus of much work in recent years. We propose to represent multisets using complex-weighted \emph{multiset automata} and show how the multiset representations of certain existing neural architectures can be viewed as special cases of ours. Namely, (1) we provide a new theoretical and intuitive justification for the Transformer model's representation of positions using sinusoidal functions, and (2) we extend the DeepSets model to use complex numbers, enabling it to outperform the existing model on an extension of one of their tasks.

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