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Title: Bicomplex neural networks with hypergeometric activation functions
Author: Vieira, N.
Keywords: Artificial neural networks and deep learning
Activation functions
Bicomplex convolutional neural networks
Hypergeometric functions
Bessel functions
Issue Date: Apr-2023
Publisher: Springer
Abstract: Bicomplex convolutional neural networks (BCCNN) are a natural extension of the quaternion convolutional neural networks for the bicomplex case. As it happens with the quaternionic case, BCCNN has the capability of learning and modelling external dependencies that exist between neighbour features of an input vector and internal latent dependencies within the feature. This property arises from the fact that, under certain circumstances, it is possible to deal with the bicomplex number in a component-wise way. In this paper, we present a BCCNN, and we apply it to a classification task involving the colorized version of the well-known dataset MNIST. Besides the novelty of considering bicomplex numbers, our CNN considers an activation function a Bessel-type function. As we see, our results present better results compared with the one where the classical ReLU activation function is considered.
Peer review: yes
DOI: 10.1007/s00006-023-01268-w
ISSN: 0188-7009
Publisher Version:
Appears in Collections:CIDMA - Artigos
CHAG - Artigos

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