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Found 4 papers, 1 papers with code
Predicting the Critical Number of Layers for Hierarchical Support Vector Regression
Hierarchical support vector regression (HSVR) models a function from data as a linear combination of SVR models at a range of scales, starting at a coarse scale and moving to finer scales as the hierarchy continues.
Applications of Koopman Mode Analysis to Neural Networks
We also show how using Koopman modes we can selectively prune the network to speed up the training procedure.
Learning low-dimensional dynamical-system models from noisy frequency-response data with Loewner rational interpolation
Loewner rational interpolation provides a versatile tool to learn low-dimensional dynamical-system models from frequency-response measurements.
Stabilizing discrete empirical interpolation via randomized and deterministic oversampling
Numerical experiments with synthetic and diffusion-reaction problems demonstrate the stability of oversampled empirical interpolation in the presence of noise.
Numerical Analysis
