Fabian Cristian Gieseke
Associate Professor
Machine Learning
Universitetsparken 1
2100 København Ø
- 2013
Towards non-linear constraint estimation for expensive optimization
Gieseke, Fabian Cristian & Kramer, O., 2013, Applications of Evolutionary Computation: 16th European Conference, EvoApplications 2013, Vienna, Austria, April 3-5, 2013. Proceedings. Esparcia-Alcázar, A. I. (ed.). Springer, p. 459-468 10 p. (Lecture notes in computer science, Vol. 7835).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
- Published
Polynomial runtime bounds for fixed-rank unsupervised least-squares classification
Gieseke, Fabian Cristian, Pahikkala, T. & Igel, Christian, 2013, Asian Conference on Machine Learning. Ong, C. S. & Ho, T. B. (eds.). Vol. 29. p. 62-71 10 p. (JMLR: Workshop and Conference Proceedings, Vol. 29).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
- Published
On GPU-based nearest neighbor queries for large-scale photometric catalogs in astronomy
Heinermann, J., Kramer, O., Polsterer, K. L. & Gieseke, Fabian Cristian, 2013, KI 2013: Advances in Artificial Intelligence: 36th Annual German Conference on AI, Koblenz, Germany, September 16-20, 2013. Proceedings. Timm, I. J. & Thimm, M. (eds.). Springer, p. 86-97 12 p. (Lecture notes in computer science, Vol. 8077).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Learning morphological maps of galaxies with unsupervised regression
Kramer, O., Gieseke, Fabian Cristian & Polsterer, K. L., 2013, In: Expert Systems with Applications. 40, 8, p. 2841-2844 4 p.Research output: Contribution to journal › Journal article › Research › peer-review
Support vector machines for wind energy prediction in smart grids
Kramer, O., Treiber, N. A. & Gieseke, Fabian Cristian, 2013, Proceedings of the 27th Conference on Environmental Informatics - Informatics for Environmental Protection, Sustainable Development and Risk Management. Shaker Verlag, p. 16-24 9 p.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Wind energy prediction and monitoring with neural computation
Kramer, O., Gieseke, Fabian Cristian & Satzger, B., 2013, In: Neurocomputing. 109, p. 84-93 10 p.Research output: Contribution to journal › Conference article › Research › peer-review
Finding new high-redshift quasars by asking the neighbours
Polsterer, K. L., Zinn, P. C. & Gieseke, Fabian Cristian, 2013, In: Monthly Notices of the Royal Astronomical Society. 428, 1, p. 226-235 10 p.Research output: Contribution to journal › Journal article › Research › peer-review
- 2014
Fast and simple gradient-based optimization for semi-supervised support vector machines
Gieseke, Fabian Cristian, Airola, A., Pahikkala, T. & Kramer, O., 2014, In: Neurocomputing. 123, p. 23-32 10 p.Research output: Contribution to journal › Journal article › Research › peer-review
- Published
Buffer k-d trees: processing massive nearest neighbor queries on GPUs
Gieseke, Fabian Cristian, Heinermann, J., Oancea, Cosmin Eugen & Igel, Christian, 2014, Proceedings of the 31st International Conference on Machine Learning, Beijing, China, 2014. 9 p. (JMLR: Workshop and Conference Proceedings, Vol. 32).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
- Published
Speedy greedy feature selection: better redshift estimation via massive parallelism
Gieseke, Fabian Cristian, Polsterer, K. L., Oancea, Cosmin Eugen & Igel, Christian, 2014, ESANN 2014 proceedings: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen, M. (ed.). i6doc.com, p. 87-92 6 p.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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Buffer k-d trees: processing massive nearest neighbor queries on GPUs
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Big universe, big data: machine learning and image analysis for astronomy
Research output: Contribution to journal › Journal article › Research › peer-review
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Creating cloud-free satellite imagery from image time series with deep learning
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Published