Optimizing traffic dynamics in an evolving transportation landscape is
c...
Kernel interpolation is a versatile tool for the approximation of functi...
Graph Neural Networks (GNNs) have become the leading paradigm for learni...
The polynomial kernels are widely used in machine learning and they are ...
In this paper we show how ideas from spline theory can be used to constr...
Following a fast initial breakthrough in graph based learning, Graph Neu...
We consider the meshless solution of PDEs via symmetric kernel collocati...
Error estimates for kernel interpolation in Reproducing Kernel Hilbert S...
Federated Leaning is an emerging approach to manage cooperation between ...
Fairness-aware GANs (FairGANs) exploit the mechanisms of Generative
Adve...
Kernel-based schemes are state-of-the-art techniques for learning by dat...
Assessing the similarity of two images is a complex task that has attrac...
Semi-lagrangian schemes for discretization of the dynamic programming
pr...
Data-dependent greedy algorithms in kernel spaces are known to provide f...
Kernel based methods yield approximation models that are flexible, effic...
Standard kernel methods for machine learning usually struggle when deali...
The inference of novel knowledge, the discovery of hidden patterns, and ...
For dynamical systems with a non hyperbolic equilibrium, it is possible ...
Greedy kernel approximation algorithms are successful techniques for spa...
In this paper we analyze a greedy procedure to approximate a linear
func...
Kernel based methods provide a way to reconstruct potentially
high-dimen...
This chapter deals with kernel methods as a special class of techniques ...
In this work, we consider two kinds of model reduction techniques to sim...
In this work, we consider two kinds of model reduction techniques to sim...
A variety of methods is available to quantify uncertainties arising with...