We consider convolutional neural networks (CNNs) with 2D structured features that are symmetric in the spatial dimensions. Such networks arise in modeling pairwise relationships for a sequential recommendation problem, as well as secondary structure inference problems of RNA and protein sequences. We develop a CNN architecture that generates and preserves the symmetry structure in the network’s convolutional layers. We present parameterizations for the convolutional kernels that produce update rules to maintain symmetry throughout the training. We apply this architecture to the sequential recommendation problem, the RNA secondary structure inference problem, and the protein contact map prediction problem, showing that the symmetric structured networks produce improved results using fewer numbers of machine parameters.
|Number of pages||14|
|Journal||Neural Computing and Applications|
|State||Published - Feb 2023|
Bibliographical noteFunding Information:
We thank the University of Kentucky Center for Computational Sciences and Information Technology Services Research Computing for their support and use of the Lipscomb Compute Cluster and associated research computing resources.
© 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
- Convolutional neural networks
- Recommendation problem
- RNA secondary structure prediction problem
ASJC Scopus subject areas
- Artificial Intelligence