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9 hours ago · Optimal learning rates of deep convolutional neural networks ... Universal function approximation by deep neural nets with bounded width and relu activations.
4 days ago · Abstract. The universal approximation property is fundamental to the success of neural networks, and has traditionally been achieved by training networks ...
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7 days ago · This implies that, when N N N italic_N is sufficiently large, a neural network can approximate any continuous function on a closed interval. Hornik et al ...
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2 days ago · 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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4 days ago · We focus on Bayesian UQ [15], [16], [17] that seeks to capture the uncertainty residing in model parameters. This entails performing Bayesian inference on the ...
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4 days ago · PDF | We propose physics-informed holomorphic neural networks (PIHNNs) as a method to solve boundary value problems where the solution can be.
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5 days ago · The versatile applications of deep neural networks in areas such as image processing (Hemanth and Estrela, 2017), object segmentation (Chen et al., 2013), ...
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3 days ago · Inspired by the adaptation phenomenon of neuronal firing, we propose the regularity normalization (RN) as an unsupervised attention mechanism (UAM) which ...
5 days ago · In this paper we investigate the use of Fourier Neural Operators (FNOs) for image classification in comparison to standard Convolutional Neural Networks (CNNs).
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