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May 4, 2024 · Yes there is a proof why deep learning, in particular neural networks, works. It is called the universal approximation theorem and it is almost 30 years old. It ...
Jul 23, 2023 · As for networks with one hidden layer, they are also universal approximators. However, the approximation theory for deep networks is less understood compared ...
May 22, 2024 · In this short note, we give an elementary proof of a universal approximation theorem for neural networks with three hidden layers and increasing, ...
Dec 20, 2023 · In Section 3, we define random neural networks as Banach space-valued random variables and show their universal approximation property. In Section 4, we prove ...
Aug 20, 2023 · The universal approximation property of Deep Neural Networks is an important ... For convolutional neural networks there has been some research into ...
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Jun 10, 2024 · PDF | The purpose of this paper is to investigate neural network capability systematically. The main results are: 1) every Tauber-Wiener function is.
Jul 15, 2023 · This paper studies the approximation capacity of ReLU neural networks with norm constraint on the weights. We prove upper and lower bounds on the ...
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Apr 24, 2024 · The range of functions that neural networks can approximate are encapsulated within the so-called universal approximation theorems (UATs)(see [57–60] for ...
Jun 18, 2024 · The universal approximation properties of INNs have also been established recently. However, the approximation rate of INNs is largely missing. In this work, we ...
Feb 16, 2024 · Abstract: Since its debut in 2016, ResNet has become arguably the most favorable architecture in deep neural network (DNN) design. It effectively addresses the ...