Fabian Gieseke
Fabian Gieseke
Professor at Department of Information Systems, University of Münster
Verified email at uni-muenster.de - Homepage
Cited by
Cited by
Buffer kd trees: processing massive nearest neighbor queries on GPUs
F Gieseke, J Heinermann, C Oancea, C Igel
International Conference on Machine Learning, 172-180, 2014
Big universe, big data: machine learning and image analysis for astronomy
J Kremer, K Stensbo-Smidt, F Gieseke, KS Pedersen, C Igel
IEEE Intelligent Systems 32 (2), 16-22, 2017
Short-term wind energy forecasting using support vector regression
O Kramer, F Gieseke
Soft Computing Models in Industrial and Environmental Applications, 6th …, 2011
Fast and simple gradient-based optimization for semi-supervised support vector machines
F Gieseke, A Airola, T Pahikkala, O Kramer
Neurocomputing 123, 23-32, 2014
On the realistic validation of photometric redshifts
R Beck, CA Lin, EEO Ishida, F Gieseke, RS de Souza, MV Costa-Duarte, ...
Monthly Notices of the Royal Astronomical Society 468 (4), 4323-4339, 2017
An unexpectedly large count of trees in the West African Sahara and Sahel
M Brandt, CJ Tucker, A Kariryaa, K Rasmussen, C Abel, J Small, J Chave, ...
Nature 587 (7832), 78-82, 2020
Wind energy prediction and monitoring with neural computation
O Kramer, F Gieseke, B Satzger
Neurocomputing 109, 84-93, 2013
Convolutional neural networks for transient candidate vetting in large-scale surveys
F Gieseke, S Bloemen, C van den Bogaard, T Heskes, J Kindler, ...
Monthly Notices of the Royal Astronomical Society 472 (3), 3101-3114, 2017
Finding new high-redshift quasars by asking the neighbours
KL Polsterer, PC Zinn, F Gieseke
Monthly Notices of the Royal Astronomical Society 428 (1), 226-235, 2013
Deep-learnt classification of light curves
A Mahabal, K Sheth, F Gieseke, A Pai, SG Djorgovski, AJ Drake, ...
2017 IEEE Symposium Series on Computational Intelligence (SSCI), 1-8, 2017
Sparse Quasi-Newton Optimization for Semi-supervised Support Vector Machines.
F Gieseke, A Airola, T Pahikkala, O Kramer
ICPRAM (1), 45-54, 2012
Attentional feature fusion
Y Dai, F Gieseke, S Oehmcke, Y Wu, K Barnard
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2021
Fast evolutionary maximum margin clustering
F Gieseke, T Pahikkala, O Kramer
Proceedings of the 26th Annual International Conference on Machine Learning …, 2009
A probabilistic approach to emission-line galaxy classification
RS De Souza, MLL Dantas, MV Costa-Duarte, ED Feigelson, M Killedar, ...
Monthly Notices of the Royal Astronomical Society 472 (3), 2808-2822, 2017
Artistic movement recognition by boosted fusion of color structure and topographic description
C Florea, C Toca, F Gieseke
2017 IEEE Winter Conference on Applications of Computer Vision (WACV), 569-577, 2017
Exploring the spectroscopic diversity of Type Ia supernovae with dracula: a machine learning approach
M Sasdelli, EEO Ishida, R Vilalta, M Aguena, VC Busti, H Camacho, ...
Monthly Notices of the Royal Astronomical Society 461 (2), 2044-2059, 2016
Return of the features-Efficient feature selection and interpretation for photometric redshifts
A D’Isanto, S Cavuoti, F Gieseke, KL Polsterer
Astronomy & Astrophysics 616, A97, 2018
Analysis of wind energy time series with kernel methods and neural networks
O Kramer, F Gieseke
2011 Seventh International Conference on Natural Computation 4, 2381-2385, 2011
Automatic galaxy classification via machine learning techniques
KL Polsterer, F Gieseke, C Igel
Astronomical Data Analysis Software and Systems, 2015
Nearest neighbor density ratio estimation for large-scale applications in astronomy
J Kremer, F Gieseke, KS Pedersen, C Igel
Astronomy and Computing 12, 67-72, 2015
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