1 citations · 1 across the 3 of their papers we have counts for
7 papers
Latent Space Exploration Using Generative Kernel PCA
David Winant, Joachim Schreurs, Johan A. K. Suykens
Kernel PCA is a powerful feature extractor which recently has seen a reformulation in the context of Restricted Kernel Machines (RKMs). These RKMs allow for a representation of ker…
Towards Deterministic Diverse Subset Sampling
Joachim Schreurs, Michaël Fanuel, Johan A. K. Suykens
Determinantal point processes (DPPs) are well known models for diverse subset selection problems, including recommendation tasks, document summarization and image search. In this p…
Leverage Score Sampling for Complete Mode Coverage in Generative Adversarial Networks
Joachim Schreurs, Hannes De Meulemeester, Michaël Fanuel +2
Commonly, machine learning models minimize an empirical expectation. As a result, the trained models typically perform well for the majority of the data but the performance may det…
Determinantal Point Processes Implicitly Regularize Semi-parametric Regression Problems
Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens
Semi-parametric regression models are used in several applications which require comprehensibility without sacrificing accuracy. Typical examples are spline interpolation in geophy…
Diversity sampling is an implicit regularization for kernel methods
Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens
Kernel methods have achieved very good performance on large scale regression and classification problems, by using the Nyström method and preconditioning techniques. The Nyström ap…
Robust Generative Restricted Kernel Machines using Weighted Conjugate Feature Duality
Arun Pandey, Joachim Schreurs, Johan A. K. Suykens
Interest in generative models has grown tremendously in the past decade. However, their training performance can be adversely affected by contamination, where outliers are encoded…