82 citations · 433 across the 35 of their papers we have counts for
5 papers · 1 filter
Differentiable Kernel Ridge Regression for Deep Learning Pipelines
Jean-Marc Mercier, Gabriele Santin
Deep neural networks dominate modern machine learning, while alternative function approximators remain comparatively underexplored at scale. In this work, we revisit kernel methods…
Reprogramming FairGANs with Variational Auto-Encoders: A New Transfer Learning Model
Beatrice Nobile, Gabriele Santin, Bruno Lepri +1
Fairness-aware GANs (FairGANs) exploit the mechanisms of Generative Adversarial Networks (GANs) to impose fairness on the generated data, freeing them from both disparate impact an…
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…