2020

 

Aufsatz (Konferenz)

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

 

Aufsatz (Konferenz)

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

 

Aufsatz (Zeitschrift)

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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Aufsatz (Konferenz)

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

 

Aufsatz (Zeitschrift)

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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Aufsatz (Konferenz)

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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