Detecting Quasars in Large-Scale Astronomical Surveys. - Fabian Gieseke; Kai Lars Polsterer; Andreas Thom 001; Peter Zinn; Dominik Bomanns; Ralf-Jurgen Dettmar; Oliver Kramer 001; Jan Vahrenhold (ICMLA, 2010)

On GPU-Based Nearest Neighbor Queries for Large-Scale Photometric Catalogs in Astronomy. - Justin Heinermann; Oliver Kramer 001; Kai Lars Polsterer; Fabian Gieseke (KI, 2013)

Learning morphological maps of galaxies with unsupervised regression. - Oliver Kramer 001; Fabian Gieseke; Kai Lars Polsterer (Expert Syst. Appl., 2013)

Speedy greedy feature selection: Better redshift estimation via massive parallelism. - Fabian Gieseke; Kai Lars Polsterer; Cosmin Eugen Oancea; Christian Igel (ESANN, 2014)

Clustering of Complex Data-Sets Using Fractal Similarity Measures and Uncertainties. - Maximilian Hoecker; Kai Lars Polsterer; Sven Dennis Kügler; Vincent Heuveline (CSE, 2015)

Autoencoding time series for visualisation. - Nikolaos Gianniotis; Sven Dennis Kügler; Peter Tiño; Kai Polsterer; Ranjeev Misra (ESANN, 2015)

Dealing with Uncertain Multimodal Photometric Redshift Estimations. - Kai Lars Polsterer (Astroinformatics, 2016)

Parallelized rotation and flipping INvariant Kohonen maps (PINK) on GPUs. - Kai Lars Polsterer; Fabian Gieseke; Christian Igel; Bernd Doser; Nikolaos Gianniotis (ESANN, 2016)

A spectral model for multimodal redshift estimation. - Sven Dennis Kügler; Nikolaos Gianniotis; Kai Lars Polsterer (SSCI, 2016)

Model-coupled autoencoder for time series visualisation. - Nikolaos Gianniotis; Sven Dennis Kügler; Peter Tiño; Kai Lars Polsterer (Neurocomputing, 2016)

Uncertain photometric redshifts via combining deep convolutional and mixture density networks. - Antonio D'Isanto; Kai Lars Polsterer (ESANN, 2017)

Massively-parallel best subset selection for ordinary least-squares regression. - Fabian Gieseke; Kai Lars Polsterer; Ashish Mahabal; Christian Igel; Tom Heskes (SSCI, 2017)

Learning Features via Transformer Networks for Cardiomyocyte Profiling. - Jan Plier; Matthias Zisler; Jennifer Furkel; Maximilian Knoll; Alexander Marx 003; Alena Fischer; Kai Polsterer; Mathias H. Konstandin; Stefania Petra (Bildverarbeitung für die Medizin, 2022)

Convolutional autoencoders for spatially-informed ensemble post-processing. - Sebastian Lerch; Kai Lars Polsterer (CoRR, 2022)

Applications of AI in Astronomy. - S. George Djorgovski; Ashish Mahabal; Matthew J. Graham; Kai Polsterer; Alberto Krone-Martins (CoRR, 2022)

Automatic Miscalibration Diagnosis: Interpreting Probability Integral Transform (PIT) Histograms. - Ondrej Podsztavek; Alexander I. Jordan; Pavel Tvrdík; Kai Lars Polsterer (ESANN, 2024)

Rotation and flipping invariant self-organizing maps with astronomical images: A cookbook and application to the VLA Sky Survey QuickLook images. - A. N. Vantyghem; Timothy J. Galvin; Sebastian Bodenstedt; Christopher P. O'dea; Yjan Gordon; M. Boyce; Lawrence Rudnick; Kai Polsterer; Heinz Andernach; M. Dionyssiou; P. Venkataraman; Ray P. Norris; et al. (Astron. Comput., 2024)

Spherinator and HiPSter: Representation Learning for Unbiased Knowledge Discovery from Simulations. - Kai Lars Polsterer; Bernd Doser; Andreas Fehlner; Sebastian Trujillo-Gomez (CoRR, 2024)

UltraPINK - New possibilities to explore Self-Organizing Kohonen Maps. - Fenja Kollasch; Kai Polsterer (CoRR, 2024)