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Found 5 papers, 1 papers with code
Finetuning greedy kernel models by exchange algorithms
Kernel based approximation offers versatile tools for high-dimensional approximation, which can especially be leveraged for surrogate modeling.
Universality and Optimality of Structured Deep Kernel Networks
In particular, we show that the use of special types of kernels yield models reminiscent of neural networks that are founded in the same theoretical framework of classical kernel methods, while enjoying many computational properties of deep neural networks.
Structured Deep Kernel Networks for Data-Driven Closure Terms of Turbulent Flows
Standard kernel methods for machine learning usually struggle when dealing with large datasets.
Biomechanical surrogate modelling using stabilized vectorial greedy kernel methods
Greedy kernel approximation algorithms are successful techniques for sparse and accurate data-based modelling and function approximation.
A novel class of stabilized greedy kernel approximation algorithms: Convergence, stability & uniform point distribution
Since the computation of an optimal selection of sampling points may be an infeasible task, one promising option is to use greedy methods.
Numerical Analysis Numerical Analysis
