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ORCIDKai Lars Polsterer
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- affiliation: Heidelberg Institute for Theoretical Studies, Germany
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2020 – today
- 2024
[j3]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, S. A. Baum, X. Rosalind Wang, Minh Huynh:
Rotation and flipping invariant self-organizing maps with astronomical images: A cookbook and application to the VLA Sky Survey QuickLook images. Astron. Comput. 47: 100824 (2024)- [c12]Ondrej Podsztavek, Alexander I. Jordan, Pavel Tvrdík, Kai Lars Polsterer:
Automatic Miscalibration Diagnosis: Interpreting Probability Integral Transform (PIT) Histograms. ESANN 2024 - [i6]Kai Lars Polsterer, Bernd Doser, Andreas Fehlner, Sebastian Trujillo-Gomez:
Spherinator and HiPSter: Representation Learning for Unbiased Knowledge Discovery from Simulations. CoRR abs/2406.03810 (2024) - [i5]Fenja Kollasch, Kai Polsterer:
UltraPINK - New possibilities to explore Self-Organizing Kohonen Maps. CoRR abs/2406.03832 (2024) - 2022
- [c11]Jan Plier, Matthias Zisler, Jennifer Furkel, Maximilian Knoll, Alexander Marx, Alena Fischer, Kai Polsterer, Mathias H. Konstandin, Stefania Petra:
Learning Features via Transformer Networks for Cardiomyocyte Profiling. Bildverarbeitung für die Medizin 2022: 167-172 - [i4]Sebastian Lerch, Kai Lars Polsterer:
Convolutional autoencoders for spatially-informed ensemble post-processing. CoRR abs/2204.05102 (2022) - [i3]S. George Djorgovski, Ashish Mahabal, Matthew J. Graham, Kai Polsterer, Alberto Krone-Martins:
Applications of AI in Astronomy. CoRR abs/2212.01493 (2022)
2010 – 2019
- 2017
- [c10]Antonio D'Isanto, Kai Lars Polsterer:
Uncertain photometric redshifts via combining deep convolutional and mixture density networks. ESANN 2017 - [c9]Fabian Gieseke, Kai Lars Polsterer, Ashish Mahabal, Christian Igel, Tom Heskes:
Massively-parallel best subset selection for ordinary least-squares regression. SSCI 2017: 1-8 - 2016
- [j2]Nikolaos Gianniotis, Sven Dennis Kügler, Peter Tiño, Kai Lars Polsterer:
Model-coupled autoencoder for time series visualisation. Neurocomputing 192: 139-146 (2016) - [c8]Kai Lars Polsterer:
Dealing with Uncertain Multimodal Photometric Redshift Estimations. Astroinformatics 2016: 156-165 - [c7]Kai Lars Polsterer, Fabian Gieseke, Christian Igel, Bernd Doser, Nikolaos Gianniotis:
Parallelized rotation and flipping INvariant Kohonen maps (PINK) on GPUs. ESANN 2016 - [c6]Sven Dennis Kügler, Nikolaos Gianniotis, Kai Lars Polsterer:
A spectral model for multimodal redshift estimation. SSCI 2016: 1-8 - [i2]Nikolaos Gianniotis, Sven Dennis Kügler, Peter Tiño, Kai Lars Polsterer:
Model-Coupled Autoencoder for Time Series Visualisation. CoRR abs/1601.05654 (2016) - 2015
- [c5]Maximilian Hoecker, Kai Lars Polsterer, Sven Dennis Kügler, Vincent Heuveline:
Clustering of Complex Data-Sets Using Fractal Similarity Measures and Uncertainties. CSE 2015: 82-91 - [c4]Nikolaos Gianniotis, Sven Dennis Kügler, Peter Tiño, Kai Polsterer, Ranjeev Misra:
Autoencoding time series for visualisation. ESANN 2015 - [i1]Nikolaos Gianniotis, Sven Dennis Kügler, Peter Tiño, Kai Polsterer, Ranjeev Misra:
Autoencoding Time Series for Visualisation. CoRR abs/1505.00936 (2015) - 2014
- [c3]Fabian Gieseke, Kai Lars Polsterer, Cosmin Eugen Oancea, Christian Igel:
Speedy greedy feature selection: Better redshift estimation via massive parallelism. ESANN 2014 - 2013
- [j1]Oliver Kramer, Fabian Gieseke, Kai Lars Polsterer:
Learning morphological maps of galaxies with unsupervised regression. Expert Syst. Appl. 40(8): 2841-2844 (2013) - [c2]Justin Heinermann, Oliver Kramer, Kai Lars Polsterer, Fabian Gieseke:
On GPU-Based Nearest Neighbor Queries for Large-Scale Photometric Catalogs in Astronomy. KI 2013: 86-97 - 2010
- [c1]Fabian Gieseke, Kai Lars Polsterer, Andreas Thom, Peter Zinn, Dominik Bomanns, Ralf-Jurgen Dettmar, Oliver Kramer, Jan Vahrenhold:
Detecting Quasars in Large-Scale Astronomical Surveys. ICMLA 2010: 352-357
Coauthor Index
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