• 2020

    Article in Journal

    Brandt, M., Tucker, C., Kariryaa, A., Rasmussen, K., Abel, C., Small, J., Chave, J., Rasmussen, L., Hiernaux, P., Diouf, A., Kergoat, L., Mertz, O., Igel, C., Gieseke, F., Schöning, J., Li, S., Melocik, K., Meyer, J., SinnoS, , Romero, E., Glennie, E., Montagu, A., Dendoncker, M., & Fensholt, R. (2020). An unexpectedly large count of non-forest trees in the Sahara and Sahel. Nature, 2020. (Accepted)
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    Conference Paper

    Gieseke, F., Rosca, S., Henriksen, T., Verbesselt, J., & Oancea, C. (2020). Massively-Parallel Change Detection for Satellite Time Series Data with Missing Values. In Proceedings of the International Conference on Data Engineering (ICDE), Dallas, USA. (Accepted)
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  • 2019

    Conference Paper

    Ko, V., Oehmcke, S., & Gieseke, F. (2019). Magnitude and Uncertainty Pruning Criterion for Neural Networks. In Proceedings of the IEEE International Conference on Big Data, Los Angeles, USA, 2317–2326.
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    Oehmcke, S., Thrysøe, C., Borgstad, A., Salles, M., Brandt, M., & Gieseke, F. (2019). Detecting Hardly Visible Roads in Low-Resolution Satellite Time Series Data. In Proceedings of the International Conference on Big Data, Los Angeles, USA, 2403–2412.
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  • 2018

    Article in Journal

    D'Isanto, A., Cavuoti, S., Gieseke, F., & Polsterer, K. (2018). Return of the features — Efficient feature selection and interpretation for photometric redshifts. Astronomy & Astrophysics, 616, A97.
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    Florea, C., & Gieseke, F. (2018). Artistic movement recognition by consensus of boosted SVM based experts. Journal of Visual Communication and Image Representation, 56, 220–233.
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    Conference Paper

    Gieseke, F., & Igel, C. (2018). Training Big Random Forests with Little Resources. In Guo, Y., & Farooq, F. (Eds.), Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2018, London, UK, August 19-23, 2018 (pp. 1445–1454). ACM.
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    Gieseke, F., Oancea, C., Mahabal, A., Igel, C., & Heskes, T. (2018). Bigger Buffer k-d Trees on Multi-Many-Core Systems. In Senger, H., Marques, O., Garcia, R., Brito, T., Iope, R., Stanzani, S., & Costa, V. (Eds.), High Performance Computing for Computational Science — VECPAR 2018 — 13th International Conference, São Pedro, Brazil, September 17-19, 2018, Revised Selected Papers (pp. 202–214). Lecture Notes in Computer Science: Vol. 11333. Springer.
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    Mehren, M., Gieseke, F., Verbesselt, J., Rosca, S., Horion, S., & Zeileis, A. (2018). Massively-parallel break detection for satellite data. In Sacharidis, D., Gamper, J., & Böhlen, M. (Eds.), Proceedings of the 30th International Conference on Scientific and Statistical Database Management, SSDBM 2018, Bozen-Bolzano, Italy, July 09-11, 2018 (pp. 5:1-5:10). ACM.
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  • 2017

    Article in Journal

    Beck, R., Lin, C., Ishida, E., Gieseke, F., Souza, R., Costa-Duarte, M., Hattab, M., & Krone-Martins, A. (2017). On the realistic validation of photometric redshifts. Monthly Notices of the Royal Astronomical Society (MNRAS), 468(4), 4323–4339.
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    Gieseke, F., Bloemen, S., Bogaard, C., Heskes, T., Kindler, J., Scalzo, R., Ribeiro, V., Roestel, J., Groot, P., Yuan, F., Möller, A., & Tucker, B. (2017). Convolutional Neural Networks for Transient Candidate Vetting in Large-Scale Surveys. Monthly Notices of the Royal Astronomical Society (MNRAS), 472(3), 3101–3114.
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    Gieseke, F., Oancea, C., & Igel, C. (2017). bufferkdtree: A Python library for massive nearest neighbor queries on multi-many-core devices. Knowledge Based Systems, 120, 1–3.
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    Kremer, J., Stensbo-Smidt, K., Gieseke, F., Pedersen, K., & Igel, C. (2017). Big Universe, Big Data: Machine Learning and Image Analysis for Astronomy. IEEE Intelligent Systems, 32(2), 16–22.
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    Souza, R., Dantas, M., Costa-Duarte, M., Feigelson, E., Killedar, M., Lablanche, P., Vilalta, R., Krone-Martins, A., Beck, R., & Gieseke, F. (2017). A probabilistic approach to emission-line galaxy classification. Monthly Notices of the Royal Astronomical Society (MNRAS), 472(3).
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    Stensbo-Smidt, K., Gieseke, F., Zirm, A., Pedersen, K., & Igel, C. (2017). Sacrificing information for the greater good: how to select photometric bands for optimal accuracy. Monthly Notices of the Royal Astronomical Society (MNRAS), 464(3), 2577–2596.
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    Conference Paper

    Florea, C., Toca, C., & Gieseke, F. (2017). Artistic Movement Recognition by Boosted Fusion of Color Structure and Topographic Description. In Proceedings of the IEEE Winter Conference on Applications of Computer Vision, Santa Rosa, CA, USA, 569–577.
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    Gieseke, F., Polsterer, K., Mahabal, A., Igel, C., & Heskes, T. (2017). Massively-parallel best subset selection for ordinary least-squares regression. In Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence, Honolulu, HI, USA, 1–8.
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    Mahabal, A., Sheth, K., Gieseke, F., Pai, A., Djorgovski, S., Drake, A., & Graham, M. (2017). Deep-learnt classification of light curves. In Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence, Honolulu, HI, USA, 1–8.
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  • 2016

    Article in Journal

    Sasdelli, M., Ishida, E., Vilalta, R., Aguena, M., Busti, V., Camacho, H., Trindade, A., Gieseke, F., Souza, R., Fantaye, Y., & Mazzali, P. (2016). Exploring the spectroscopic diversity of type Ia supernovae with DRACULA: A machine learning approach. Monthly Notices of the Royal Astronomical Society (MNRAS), 461(2), 2044–2059.
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    Conference Paper

    Ishida, E., Sasdelli, M., Vilalta, R., Aguena, M., Busti, V., Camacho, H., Trindade, A., Gieseke, F., Souza, R., Fantaye, Y., & Mazzali, P. (2016). Exploring the spectroscopic diversity of type Ia supernovae with Deep Learning and Unsupervised Clustering. In Brescia, M., Djorgovski, S., Feigelson, E., Longo, G., & Cavuoti, S. (Eds.), Proceedings of the International Astronomical Union (pp. 247–252). Proceedings of the International Astronomical Union: Vol. 12. Cambridge University Press.
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    Polsterer, K., Gieseke, F., Igel, C., Doser, B., & Gianniotis, N. (2016). Parallelized rotation and flipping INvariant Kohonen maps (PINK) on GPUs. In Proceedings of the 24th European Symposium on Artificial Neural Networks, Bruges, Belgium.
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  • 2015

    Article in Journal

    Kremer, J., Gieseke, F., Pedersen, K., & Igel, C. (2015). Nearest Neighbor Density Ratio Estimation for Large-Scale Applications in Astronomy. Astronomy and Computing, 12, 62–72.
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    Conference Paper

    Gieseke, F. (2015). An Efficient Many-Core Implementation for Semi-Supervised Support Vector Machines. In Pardalos, P., Pavone, M., Farinella, G., & Cutello, V. (Eds.), Machine Learning, Optimization, and Big Data — First International Workshop, MOD 2015, Taormina, Sicily, Italy, July 21-23, 2015, Revised Selected Papers (pp. 145–157). Lecture Notes in Computer Science: Vol. 9432. Springer.
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    Gieseke, F., Pahikkala, T., & Heskes, T. (2015). Batch Steepest-Descent-Mildest-Ascent for Interactive Maximum Margin Clustering. In Fromont, B. T., & Leeuwen, M. (Eds.), Advances in Intelligent Data Analysis XIV — 14th International Symposium, IDA 2015, Saint Etienne, France, October 22-24, 2015, Proceedings (pp. 95–107). Lecture Notes in Computer Science: Vol. 9385. Springer.
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  • 2014

    Article in Journal

    Gieseke, F., Airola, A., Pahikkala, T., & Kramer, O. (2014). Fast and simple gradient-based optimization for semi-supervised support vector machines. Neurocomputing, 123, 23–32.
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    Pahikkala, T., Airola, A., Gieseke, F., & Kramer, O. (2014). On Unsupervised Training of Multi-Class Regularized Least-Squares Classifiers. Journal of Computer Science and Technology (ICDM 2012 Special Issue), 29(1), 90–104.
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    Conference Paper

    Gieseke, F., Heinermann, J., Oancea, C., & Igel, C. (2014). Buffer k-d Trees: Processing Massive Nearest Neighbor Queries on GPUs. In Proceedings of the 31th International Conference on Machine Learning, Beijing, China, 172–180.
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    Gieseke, F., Polsterer, K., Oancea, C., & Igel, C. (2014). Speedy greedy feature selection: Better redshift estimation via massive parallelism. In Proceedings of the 22th European Symposium on Artificial Neural Networks, Bruges, Belgium.
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    Kramer, O., Gieseke, F., Heinermann, J., Poloczek, J., & Treiber, N. (2014). A Framework for Data Mining in Wind Power Time Series. In Woon, W., Aung, Z., & Madnick, S. (Eds.), Data Analytics for Renewable Energy Integration — Second ECML PKDD Workshop, DARE 2014, Nancy, France, September 19, 2014, Revised Selected Papers (pp. 97–107). Lecture Notes in Computer Science: Vol. 8817. Springer.
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  • 2013

    Article in Journal

    Gieseke, F. (2013). From Supervised to Unsupervised Support Vector Machines and Applications in Astronomy. KI, 27(3), 281–285.
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    Gieseke, F. (2013). Von überwachten zu unüberwachten Support-Vektor-Maschinen und Anwendungen in der Astronomie. Ausgezeichnete Informatikdissertationen 2012, D-13, 111–120.
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    Kramer, O., Gieseke, F., & Polsterer, K. (2013). Learning morphological maps of galaxies with unsupervised regression. Expert Systems with Applications, 40(8), 2841–2844.
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    Kramer, O., Gieseke, F., & Satzger, B. (2013). Wind energy prediction and monitoring with neural computation. Neurocomputing, 109, 84–93.
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    Polsterer, K., Zinn, P., & Gieseke, F. (2013). Finding New High-Redshift Quasars by Asking the Neighbours. Monthly Notices of the Royal Astronomical Society (MNRAS), 428(1), 226–235.
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    Conference Paper

    Gieseke, F., & Kramer, O. (2013). Towards Non-linear Constraint Estimation for Expensive Optimization. In Esparcia{-}Alc{á}zar, A. (Ed.), Applications of Evolutionary Computation — 16th European Conference, EvoApplications 2013, Vienna, Austria, April 3-5, 2013. Proceedings (pp. 459–468). Lecture Notes in Computer Science: Vol. 7835. Springer.
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    Gieseke, F., Pahikkala, T., & Igel, C. (2013). Polynomial Runtime Bounds for Fixed-Rank Unsupervised Least-Squares Classification. In Ong, C., & Ho, T. (Eds.), Asian Conference on Machine Learning, ACML 2013, Canberra, ACT, Australia, November 13-15, 2013 (pp. 62–71). JMLR Workshop and Conference Proceedings: Vol. 29. JMLR.org.
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    Heinermann, J., Kramer, O., Polsterer, K., & Gieseke, F. (2013). On GPU-Based Nearest Neighbor Queries for Large-Scale Photometric Catalogs in Astronomy. In Timm, I., & Thimm, M. (Eds.), KI 2013: Advances in Artificial Intelligence — 36th Annual German Conference on AI, Koblenz, Germany, September 16-20, 2013. Proceedings (pp. 86–97). Lecture Notes in Computer Science: Vol. 8077. Springer.
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    Kramer, O., Treiber, N., & Gieseke, F. (2013). Support Vector Machines for Wind Energy Prediction in Smart Grids. In Page, B., Fleischer, A., Göbel, J., & Wohlgemuth, V. (Eds.), 27th International Conference on Environmental Informatics for Environmental Protection, Sustainable Development and Risk Management, EnviroInfo 2013, Hamburg, Germany, September 2-4, 2013. Proceedings (pp. 16–24). Shaker.
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  • 2012

    Article in Journal

    Gieseke, F., Kramer, O., Airola, A., & Pahikkala, T. (2012). Efficient recurrent local search strategies for semi- and unsupervised regularized least-squares classification. Evolutionary Intelligence, 5(3), 189–205.
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    Gieseke, F., Moruz, G., & Vahrenhold, J. (2012). Resilient k-d trees: k-means in space revisited. Frontiers of Computer Science (ICDM 2010 Special Issue), 6(2), 166–178.
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    Kramer, O., & Gieseke, F. (2012). Evolutionary kernel density regression. Expert Systems and Applications, 39(10), 9246–9254.
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    Conference Paper

    Gieseke, F., Airola, A., Pahikkala, T., & Kramer, O. (2012). Sparse Quasi-Newton Optimization for Semi-supervised Support Vector Machines. In Carmona, P., Sánchez, J., & Fred, A. (Eds.), ICPRAM 2012 — Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods, Volume 1, Vilamoura, Algarve, Portugal, 6-8 February, 2012 (pp. 45–54). SciTePress.
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    Pahikkala, T., Airola, A., Gieseke, F., & Kramer, O. (2012). Unsupervised Multi-class Regularized Least-Squares Classification. In Zaki, M., Siebes, A., Yu, J., Goethals, B., Webb, G., & Wu, X. (Eds.), 12th IEEE International Conference on Data Mining, ICDM 2012, Brussels, Belgium, December 10-13, 2012 (pp. 585–594). IEEE Computer Society.
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    Thesis

    Gieseke, F. (2012). From supervised to unsupervised support vector machines and applications in astronomy. Dissertation at the Carl von Ossietzky University of Oldenburg.
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  • 2011

    Conference Paper

    Gieseke, F., Kramer, O., Airola, A., & Pahikkala, T. (2011). Speedy Local Search for Semi-Supervised Regularized Least-Squares. In Bach, J., & Edelkamp, S. (Eds.), KI 2011: Advances in Artificial Intelligence, 34th Annual German Conference on AI, Berlin, Germany, October 4-7,2011. Proceedings (pp. 87–98). Lecture Notes in Computer Science: Vol. 7006. Springer.
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    Kramer, O., & Gieseke, F. (2011). Analysis of wind energy time series with kernel methods and neural networks. In Ding, Y., Wang, H., Xiong, N., Hao, K., & Wang, L. (Eds.), Seventh International Conference on Natural Computation, ICNC 2011, Shanghai, China, 26-28 July, 2011 (pp. 2381–2385). IEEE.
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    Kramer, O., & Gieseke, F. (2011). Variance Scaling for EDAs Revisited. In Bach, J., & Edelkamp, S. (Eds.), KI 2011: Advances in Artificial Intelligence, 34th Annual German Conference on AI, Berlin, Germany, October 4-7,2011. Proceedings (pp. 169–178). Lecture Notes in Computer Science: Vol. 7006. Springer.
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    Kramer, O., & Gieseke, F. (2011). Short-Term Wind Energy Forecasting Using Support Vector Regression. In Corchado, E., Sn{á}sel, V., Sedano, J., Hassanien, A., Calvo{-}Rolle, J., & Slezak, D. (Eds.), Soft Computing Models in Industrial and Environmental Applications, 6th International Conference {SOCO} 2011, 6-8 April, 2011, Salamanca, Spain (pp. 271–280). Advances in Intelligent and Soft Computing: Vol. 87. Springer.
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  • 2010

    Article in Journal

    Gieseke, F., Gudmundsson, J., & Vahrenhold, J. (2010). Pruning Spanners and Constructing Well-Separated Pair Decompositions in the Presence of Memory Hierarchies. Journal of Discrete Algorithms (JDA), 8(3), 259–272.
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    Conference Paper

    Gieseke, F., Moruz, G., & Vahrenhold, J. (2010). Resilient K-d Trees: K-Means in Space Revisited. In Proceedings of the 10th IEEE International Conference on Data Mining (ICDM), Sydney, Australia, 815–820.
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    Gieseke, F., Polsterer, K., Thom, A., Zinn, P., Bomans, D., Dettmar, R.-J., Kramer, O., & Vahrenhold, J. (2010). Detecting Quasars in Large-Scale Astronomical Surveys. In Proceedings of the 9th International Conference on Machine Learning and Applications (ICMLA), Washington D.C., USA, 352–357.
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  • 2009

    Conference Paper

    Gieseke, F., Pahikkala, T., & Kramer, O. (2009). Fast evolutionary maximum margin clustering. In Proceedings of the 26th International Conference on Machine Learning (ICML 2009), Montreal, Canada, 361–368.
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