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Jul 2, 2024 · In this paper, we study random neural networks which are single-hidden-layer feedforward neural networks whose weights and biases are randomly initialized.
Jul 15, 2024 · Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and its application to dynamical systems. IEEE ...
Jul 1, 2024 · This paper extends the universal approximation theorem for a broad class of hypercomplex-valued neural networks. Precisely, we first introduce the concept of ...
Jul 8, 2024 · Approximation properties of neural networks are established (universal approximation property), and the approximation error is related to the number of ...
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Jul 18, 2024 · Artificial neural networks, currently known as deep learning models, are universal approximators from a mathematical point of view [5] [6] [7] [8]. Essentially, ...
Jul 6, 2024 · Our work lays foundation for the complete quantum implementation of classical residual neural networks and offers a quantum feature map strategy for quantum ...
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Jul 19, 2024 · As the universal approximation theorem indicates, NNs can solve equations by approximating nonlinear functions that satisfy given constraints. Various network ...
Jul 10, 2024 · A survey of approximation based hardware acceleration techniques for deep neural networks (Invited). In Proceedings of the 16th IEEE International ...
6 days ago · Physics-informed neural networks (PINNs), also referred to as Theory-Trained Neural Networks (TTNs), are a type of universal function approximators that can ...
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On the approximation capability of GNNs in node classification ...
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Jul 5, 2024 · The universal approximation property has been extended to Folklore Graph Neural Networks in Maron et al. ... Most of GNN models, e.g., Graph Convolutional Neural ...
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