Spectral complexity-scaled generalisation bound of complex-valued neural networks

نویسندگان

چکیده

Complex-valued neural networks (CVNNs) have been widely applied in various fields, primarily signal processing and image recognition. Few studies focused on the generalisation of CVNNs, although it is vital to ensure performance CVNNs unseen data. This study first prove a bound for complex-valued networks. The bounds increase as spectral complexity increases, with dominant factor being product norms weight matrices. Furthermore, this work provides trained sequential data, which also affected by complexity. Theoretically, these are derived using Maurey Sparsification Lemma Dudley entropy integral. We conducted empirical experiments datasets including MNIST, ashionMNIST, CIFAR-10, CIFAR-100, Tiny ImageNet, IMDB training convolutional Spearman rank-order correlation coefficient corresponding p-values provide strong proof statistically significant between network its ability, measured norm code available at https://github.com/LeavesLei/cvnn_generalization.

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ژورنال

عنوان ژورنال: Artificial Intelligence

سال: 2023

ISSN: ['2633-1403']

DOI: https://doi.org/10.1016/j.artint.2023.103951