1k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Goddard Space Flight CenterUS21 papers
- Centre National de la Recherche ScientifiqueFR18 papers
- Commissariat à l'Énergie Atomique et aux Énergies AlternativesFR18 papers
- Astrophysique, Instrumentation et ModélisationFR17 papers
- Max Planck Institute for Radio AstronomyDE17 papers
- Universities Space Research AssociationUS17 papers
- National Institute for AstrophysicsIT16 papers
- University of Maryland, College ParkUS16 papers
- University of PaduaIT16 papers
- University of Rome Tor VergataIT16 papers
- CEA Paris-SaclayFR15 papers
- Friedrich-Alexander-Universität Erlangen-NürnbergDE15 papers
5 papers · 2 filters
Filter Methods for Feature Selection in Supervised Machine Learning Applications -- Review and Benchmark
Konstantin Hopf, Sascha Reifenrath
The amount of data for machine learning (ML) applications is constantly growing. Not only the number of observations, especially the number of measured variables (features) increas…
Self-explaining variational posterior distributions for Gaussian Process models
Sarem Seitz
Bayesian methods have become a popular way to incorporate prior knowledge and a notion of uncertainty into machine learning models. At the same time, the complexity of modern machi…
Expressive Explanations of DNNs by Combining Concept Analysis with ILP
Johannes Rabold, Gesina Schwalbe, Ute Schmid
Explainable AI has emerged to be a key component for black-box machine learning approaches in domains with a high demand for reliability or transparency. Examples are medical assis…
A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Concepts
Gesina Schwalbe, Bettina Finzel
In the meantime, a wide variety of terminologies, motivations, approaches, and evaluation criteria have been developed within the research field of explainable artificial intellige…
Mixtures of Gaussian Processes for regression under multiple prior distributions
Sarem Seitz
When constructing a Bayesian Machine Learning model, we might be faced with multiple different prior distributions and thus are required to properly consider them in a sensible man…