Fabian Cristian Gieseke
Associate Professor
Machine Learning
Universitetsparken 1
2100 København Ø
- 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
- Published
A framework for data mining in wind power time series
Kramer, O., Gieseke, Fabian Cristian, Heinermann, J., Poloczek, J. & Treiber, N. A., 2014, Data analytics for renewable energy integration: Second ECML PKDD Workshop, DARE 2014, Nancy, France, September 19, 2014, Revised Selected Papers. Woon, W. L., Aung, Z. & Madnick, S. (eds.). Springer, p. 97-107 (Lecture notes in computer science, Vol. 8817).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
- Published
On unsupervised training of multi-class regularized least-squares classifiers
Pahikkala, T., Airola, A., Gieseke, Fabian Cristian & Kramer, O., 2014, In: Journal of Computer Science and Technology. 29, 1, p. 90-104 15 p.Research output: Contribution to journal › Journal article › Research › peer-review
- Published
Improving the performance of photometric regression models via massive parallel feature selection
Polsterer, K. L., Gieseke, Fabian Cristian, Igel, Christian & Goto, T., 2014, Astronomical Data Analysis Software and Systems XXIII. Manset, N. & Forshay, P. (eds.). Astronomical Society of the Pacific, Vol. 485. p. 425-428 4 p. (Astronomical Society of the Pacific. Conference Proceedings, Vol. 485).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
ID: 91244670
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Buffer k-d trees: processing massive nearest neighbor queries on GPUs
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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246
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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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119
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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