Abstract: In this article, we aim to offer an interpretable learning paradigm for incremental random weight neural networks (IRWNNs). IRWNNs have become a hot research direction of neural network ...
Abstract: Biomaterials are fundamental to contemporary medical technology, providing the foundation for a broad spectrum of interventions aimed at improving, restoring, or replacing impaired ...
Abstract: Deep supervised learning algorithms typically require a large volume of labeled data to achieve satisfactory performance. However, the process of collecting and labeling such data can be ...
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