82 citations · 433 across the 35 of their papers we have counts for
7 papers · 1 filter
Greedy algorithms for learning via exponential-polynomial splines
Rosanna Campagna, Stefano De Marchi, Emma Perracchione +1
Kernel-based schemes are state-of-the-art techniques for learning by data. In this work we extend some ideas about kernel-based greedy algorithms to exponential-polynomial splines,…
HJB-RBF based approach for the control of PDEs
Alessandro Alla, Hugo Oliveira, Gabriele Santin
Semi-lagrangian schemes for discretization of the dynamic programming principle are based on a time discretization projected on a state-space grid. The use of a structured grid mak…
Convergence analysis for image interpolation in terms of the cSSIM
Francesco Marchetti, Gabriele Santin
Assessing the similarity of two images is a complex task that attracts significant efforts in the image processing community. The widely used Structural Similarity Index Measure (S…
Analysis of target data-dependent greedy kernel algorithms: Convergence rates for -, - and -greedy
Tizian Wenzel, Gabriele Santin, Bernard Haasdonk
Data-dependent greedy algorithms in kernel spaces are known to provide fast converging interpolants, while being extremely easy to implement and efficient to run. Despite this expe…
Analysis of Structured Deep Kernel Networks
Tizian Wenzel, Gabriele Santin, Bernard Haasdonk
In this paper, we leverage a recent deep kernel representer theorem to connect kernel based learning and (deep) neural networks in order to understand their interplay. In particula…
Structured Deep Kernel Networks for Data-Driven Closure Terms of Turbulent Flows
Tizian Wenzel, Marius Kurz, Andrea Beck +2
Standard kernel methods for machine learning usually struggle when dealing with large datasets. We review a recently introduced Structured Deep Kernel Network (SDKN) approach that…
Kernel-Based Models for Influence Maximization on Graphs based on Gaussian Process Variance Minimization
Salvatore Cuomo, Wolfgang Erb, Gabriele Santin
The inference of novel knowledge, the discovery of hidden patterns, and the uncovering of insights from large amounts of data from a multitude of sources make Data Science (DS) to…