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math.NA2025
Convergence for adaptive resampling of random Fourier features
Xin Huang, Aku Kammonen, Anamika Pandey +4
The machine learning random Fourier feature method for data in high dimension is computationally and theoretically attractive since the optimization is based on a convex standard l…
math.NA2020
Smaller generalization error derived for a deep residual neural network compared to shallow networks
Aku Kammonen, Jonas Kiessling, Petr Plecháč +3
Estimates of the generalization error are proved for a residual neural network with random Fourier features layers $\bar z_{\ell+1}=\bar z_\ell + \mathrm{Re}\sum_{k=1}^K\bar b_…
math.NA2020
Adaptive random Fourier features with Metropolis sampling
Aku Kammonen, Jonas Kiessling, Petr Plecháč +2
The supervised learning problem to determine a neural network approximation with one hidden layer is studied…