Operator theory, kernels, and Feedforward Neural Networks
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Abstract (arXiv)
In this paper we show how specific families of positive definite kernels serve as powerful tools in analyses of iteration algorithms for multiple layer feedforward Neural Network models. Our focus is on particular kernels that adapt well to learning algorithms for data-sets/features which display intrinsic self-similarities at feedforward iterations of scaling.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 10 of its 53 references that are in the map. First posted on arXiv 3 Jan 2023.
Builds on
10 of 53 references.
References · 10
- 2017Nonparametric regression using deep neural networks with ReLU activation functioncited by 431
- 2020Tighter risk certificates for neural networkscited by 112
- 2019Error bounds for approximations with deep ReLU neural networks in Ws,p normscited by 95
- 2019Approximation spaces of deep neural networkscited by 51
- 2020Numerical Solution of the Parametric Diffusion Equation by Deep Neural Networkscited by 27
- 2020Learning Deep ReLU Networks Is Fixed-Parameter Tractablecited by 26
- 2021Convergence analysis for gradient flows in the training of artificial neural networks with ReLU activationcited by 11
- 2021A proof of convergence for stochastic gradient descent in the training of artificial neural networks with ReLU activation for constant target functionscited by 9
- 2019Stochastic Feedforward Neural Networks: Universal Approximationcited by 7
- 2021Learning in Feedforward Neural Networks Accelerated by Transfer Entropycited by 5
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Citing papers per year: 2026: 2.
2 papers.