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2020 – today
- 2024
[j17]Denis Mayr Lima Martins
, Christian Lülf, Fabian Gieseke:
Training neural networks end-to-end for hyperbox-based classification. Neurocomputing 599: 127961 (2024)- [c42]Jan Pauls, Max Zimmer, Una M. Kelly, Martin Schwartz, Sassan Saatchi, Philippe Ciais, Sebastian Pokutta, Martin Brandt, Fabian Gieseke:
Estimating Canopy Height at Scale. ICML 2024 - [c41]Christian Lülf, Denis Mayr Lima Martins, Marcos Antonio Vaz Salles, Yongluan Zhou, Fabian Gieseke:
CLIP-Branches: Interactive Fine-Tuning for Text-Image Retrieval. SIGIR 2024: 2719-2723 - [i18]Jan Pauls, Max Zimmer, Una M. Kelly, Martin Schwartz, Sassan Saatchi, Philippe Ciais, Sebastian Pokutta, Martin Brandt, Fabian Gieseke:
Estimating Canopy Height at Scale. CoRR abs/2406.01076 (2024) - [i17]Christian Lülf, Denis Mayr Lima Martins, Marcos Antonio Vaz Salles, Yongluan Zhou, Fabian Gieseke:
CLIP-Branches: Interactive Fine-Tuning for Text-Image Retrieval. CoRR abs/2406.13322 (2024) - 2023
- [j16]Christian Lülf, Denis Mayr Lima Martins, Marcos Antonio Vaz Salles, Yongluan Zhou, Fabian Gieseke:
Fast Search-By-Classification for Large-Scale Databases Using Index-Aware Decision Trees and Random Forests. Proc. VLDB Endow. 16(11): 2845-2857 (2023) - [c40]Dmitry Serykh, Stefan Oehmcke, Cosmin E. Oancea, Dainius Masiliunas, Jan Verbesselt, Yan Cheng, Stéphanie Horion, Fabian Gieseke, Nikolaj Hinnerskov:
Seasonal-Trend Time Series Decomposition on Graphics Processing Units. IEEE Big Data 2023: 5914-5923 - [c39]Denis Martins, Christian Lülf, Fabian Gieseke:
End-to-End Neural Network Training for Hyperbox-Based Classification. ESANN 2023 - [c38]Christian Lülf, Denis Mayr Lima Martins, Marcos Antonio Vaz Salles, Yongluan Zhou, Fabian Gieseke:
RapidEarth: A Search-by-Classification Engine for Large-Scale Geospatial Imagery. SIGSPATIAL/GIS 2023: 58:1-58:4 - [i16]Lei Li, Tianfang Zhang, Stefan Oehmcke, Fabian Gieseke, Christian Igel:
BuildSeg: A General Framework for the Segmentation of Buildings. CoRR abs/2301.06190 (2023) - [i15]Christian Lülf, Denis Mayr Lima Martins, Marcos Antonio Vaz Salles, Yongluan Zhou, Fabian Gieseke:
Fast Search-By-Classification for Large-Scale Databases Using Index-Aware Decision Trees and Random Forests. CoRR abs/2306.02670 (2023) - [i14]Denis Mayr Lima Martins, Christian Lülf, Fabian Gieseke:
End-to-End Neural Network Training for Hyperbox-Based Classification. CoRR abs/2307.09269 (2023) - [i13]Christian Lülf, Denis Mayr Lima Martins, Marcos Antonio Vaz Salles, Yongluan Zhou, Fabian Gieseke:
RapidEarth: A Search-by-Classification Engine for Large-Scale Geospatial Imagery. CoRR abs/2309.15617 (2023) - 2022
- [j15]Jaime C. Revenga, Katerina Trepekli, Stefan Oehmcke, Rasmus Jensen, Lei Li, Christian Igel, Fabian Gieseke, Thomas Friborg:
Above-Ground Biomass Prediction for Croplands at a Sub-Meter Resolution Using UAV-LiDAR and Machine Learning Methods. Remote. Sens. 14(16): 3912 (2022) - [c37]Stefan Oehmcke, Lei Li, Jaime C. Revenga, Thomas Nord-Larsen, Katerina Trepekli, Fabian Gieseke, Christian Igel:
Deep learning based 3D point cloud regression for estimating forest biomass. SIGSPATIAL/GIS 2022: 38:1-38:4 - [c36]Stefan Oehmcke, Fabian Gieseke:
Input Selection for Bandwidth-Limited Neural Network Inference. SDM 2022: 280-288 - [i12]Tianfang Zhang, Lei Li, Christian Igel, Stefan Oehmcke, Fabian Gieseke, Zhenming Peng:
LR-CSNet: Low-Rank Deep Unfolding Network for Image Compressive Sensing. CoRR abs/2212.09088 (2022) - 2021
- [c35]Stefan Oehmcke, Thomas Nyegaard-Signori, Kenneth Grogan, Fabian Gieseke:
Estimating Forest Canopy Height With Multi-Spectral and Multi-Temporal Imagery Using Deep Learning. IEEE BigData 2021: 4915-4924 - [c34]Philip Munksgaard, Svend Lund Breddam, Troels Henriksen, Fabian Gieseke, Cosmin E. Oancea:
Dataset Sensitive Autotuning of Multi-versioned Code Based on Monotonic Properties - Autotuning in Futhark. TFP 2021: 3-23 - [c33]Yimian Dai, Fabian Gieseke, Stefan Oehmcke, Yiquan Wu, Kobus Barnard:
Attentional Feature Fusion. WACV 2021: 3559-3568 - [i11]Stefan Oehmcke, Lei Li, Jaime C. Revenga, Thomas Nord-Larsen, Katerina Trepekli, Fabian Gieseke, Christian Igel:
Deep Learning Based 3D Point Cloud Regression for Estimating Forest Biomass. CoRR abs/2112.11335 (2021) - 2020
- [j14]Eliakim Hamunyela, Sabina Rosca, Andrei Mîrt, Eric Engle, Martin Herold, Fabian Gieseke, Jan Verbesselt:
Implementation of BFASTmonitor Algorithm on Google Earth Engine to Support Large-Area and Sub-Annual Change Monitoring Using Earth Observation Data. Remote. Sens. 12(18): 2953 (2020) - [c32]Cosmin Eugen Oancea, Ties Robroek, Fabian Gieseke:
Approximate Nearest-Neighbour Fields via Massively-Parallel Propagation-Assisted K-D Trees. IEEE BigData 2020: 5172-5181 - [c31]Stefan Oehmcke, Tzu-Hsin Karen Chen, Alexander V. Prishchepov, Fabian Gieseke:
Creating cloud-free satellite imagery from image time series with deep learning. BigSpatial@SIGSPATIAL 2020: 3:1-3:10 - [c30]Fabian Gieseke, Sabina Rosca, Troels Henriksen, Jan Verbesselt, Cosmin E. Oancea:
Massively-Parallel Change Detection for Satellite Time Series Data with Missing Values. ICDE 2020: 385-396 - [c29]Yimian Dai, Stefan Oehmcke, Fabian Gieseke, Yiquan Wu, Kobus Barnard:
Attention as Activation. ICPR 2020: 9156-9163 - [i10]Nikita Moriakov, Ashwin Samudre, Michela Negro, Fabian Gieseke, Sydney Otten, Luc Hendriks:
Inferring astrophysical X-ray polarization with deep learning. CoRR abs/2005.08126 (2020) - [i9]Yimian Dai, Fabian Gieseke, Stefan Oehmcke, Yiquan Wu, Kobus Barnard:
Attentional Feature Fusion. CoRR abs/2009.14082 (2020)
2010 – 2019
- 2019
- [c28]Vinnie Ko, Stefan Oehmcke, Fabian Gieseke:
Magnitude and Uncertainty Pruning Criterion for Neural Networks. IEEE BigData 2019: 2317-2326 - [c27]Stefan Oehmcke, Christoffer Thrysøe, Andreas Borgstad, Marcos Antonio Vaz Salles, Martin Brandt, Fabian Gieseke:
Detecting Hardly Visible Roads in Low-Resolution Satellite Time Series Data. IEEE BigData 2019: 2403-2412 - [i8]Stefan Oehmcke, Fabian Gieseke:
Learning Selection Masks for Deep Neural Networks. CoRR abs/1906.04673 (2019) - [i7]Vinnie Ko, Stefan Oehmcke, Fabian Gieseke:
Magnitude and Uncertainty Pruning Criterion for Neural Networks. CoRR abs/1912.04845 (2019) - [i6]Stefan Oehmcke, Christoffer Thrysøe, Andreas Borgstad, Marcos Antonio Vaz Salles, Martin Brandt, Fabian Gieseke:
Detecting Hardly Visible Roads in Low-Resolution Satellite Time Series Data. CoRR abs/1912.05026 (2019) - 2018
- [j13]Corneliu Florea, Fabian Gieseke:
Artistic movement recognition by consensus of boosted SVM based experts. J. Vis. Commun. Image Represent. 56: 220-233 (2018) - [c26]Fabian Gieseke, Christian Igel:
Training Big Random Forests with Little Resources. KDD 2018: 1445-1454 - [c25]Malte von Mehren, Fabian Gieseke, Jan Verbesselt, Sabina Rosca, Stéphanie Horion, Achim Zeileis:
Massively-parallel break detection for satellite data. SSDBM 2018: 5:1-5:10 - [c24]Fabian Gieseke, Cosmin Eugen Oancea, Ashish Mahabal, Christian Igel, Tom Heskes:
Bigger Buffer k-d Trees on Multi-Many-Core Systems. VECPAR 2018: 202-214 - [i5]Fabian Gieseke, Christian Igel:
Training Big Random Forests with Little Resources. CoRR abs/1802.06394 (2018) - [i4]Malte von Mehren, Fabian Gieseke, Jan Verbesselt, Sabina Rosca, Stéphanie Horion, Achim Zeileis:
Massively-Parallel Break Detection for Satellite Data. CoRR abs/1807.01751 (2018) - 2017
- [j12]Jan Kremer, Kristoffer Stensbo-Smidt, Fabian Gieseke, Kim Steenstrup Pedersen, Christian Igel:
Big Universe, Big Data: Machine Learning and Image Analysis for Astronomy. IEEE Intell. Syst. 32(2): 16-22 (2017) - [j11]Fabian Gieseke, Cosmin E. Oancea, Christian Igel:
bufferkdtree: A Python library for massive nearest neighbor queries on multi-many-core devices. Knowl. Based Syst. 120: 1-3 (2017) - [c23]Fabian Gieseke, Kai Lars Polsterer, Ashish Mahabal, Christian Igel, Tom Heskes:
Massively-parallel best subset selection for ordinary least-squares regression. SSCI 2017: 1-8 - [c22]Ashish Mahabal, Kshiteej Sheth, Fabian Gieseke, Akshay Pai, S. George Djorgovski, Andrew J. Drake, Matthew J. Graham:
Deep-learnt classification of light curves. SSCI 2017: 1-8 - [c21]Corneliu Florea, Cosmin Toca, Fabian Gieseke:
Artistic Movement Recognition by Boosted Fusion of Color Structure and Topographic Description. WACV 2017: 569-577 - [i3]Jan Kremer, Kristoffer Stensbo-Smidt, Fabian Gieseke, Kim Steenstrup Pedersen, Christian Igel:
Big Universe, Big Data: Machine Learning and Image Analysis for Astronomy. CoRR abs/1704.04650 (2017) - [i2]Ashish Mahabal, Kshiteej Sheth, Fabian Gieseke, Akshay Pai, S. George Djorgovski, Andrew J. Drake, Matthew J. Graham:
Deep-Learnt Classification of Light Curves. CoRR abs/1709.06257 (2017) - 2016
- [c20]Emille E. O. Ishida, Michele Sasdelli, Ricardo Vilalta, Michel Aguena, Vinicius C. Busti, Hugo Camacho, Arlindo M. M. Trindade, Fabian Gieseke, Rafael S. de Souza, Yabebal T. Fantaye, Paolo A. Mazzali:
Exploring the spectroscopic diversity of type Ia supernovae with Deep Learning and Unsupervised Clustering. Astroinformatics 2016: 247-252 - [c19]Kai Lars Polsterer, Fabian Gieseke, Christian Igel, Bernd Doser, Nikolaos Gianniotis:
Parallelized rotation and flipping INvariant Kohonen maps (PINK) on GPUs. ESANN 2016 - 2015
- [j10]Jan Kremer, Fabian Gieseke, Kim Steenstrup Pedersen, Christian Igel:
Nearest neighbor density ratio estimation for large-scale applications in astronomy. Astron. Comput. 12: 67-72 (2015) - [c18]Fabian Gieseke, Tapio Pahikkala, Tom Heskes:
Batch Steepest-Descent-Mildest-Ascent for Interactive Maximum Margin Clustering. IDA 2015: 95-107 - [c17]Fabian Gieseke:
An Efficient Many-Core Implementation for Semi-Supervised Support Vector Machines. MOD 2015: 145-157 - [i1]Fabian Gieseke, Cosmin Eugen Oancea, Ashish Mahabal, Christian Igel, Tom Heskes:
Bigger Buffer k-d Trees on Multi-Many-Core Systems. CoRR abs/1512.02831 (2015) - 2014
- [j9]Fabian Gieseke, Antti Airola, Tapio Pahikkala, Oliver Kramer:
Fast and simple gradient-based optimization for semi-supervised support vector machines. Neurocomputing 123: 23-32 (2014) - [j8]Tapio Pahikkala, Antti Airola, Fabian Gieseke, Oliver Kramer:
On Unsupervised Training of Multi-Class Regularized Least-Squares Classifiers. J. Comput. Sci. Technol. 29(1): 90-104 (2014) - [c16]Fabian Gieseke, Kai Lars Polsterer, Cosmin Eugen Oancea, Christian Igel:
Speedy greedy feature selection: Better redshift estimation via massive parallelism. ESANN 2014 - [c15]Fabian Gieseke, Justin Heinermann, Cosmin E. Oancea, Christian Igel:
Buffer k-d Trees: Processing Massive Nearest Neighbor Queries on GPUs. ICML 2014: 172-180 - [c14]Oliver Kramer, Fabian Gieseke, Justin Heinermann, Jendrik Poloczek, Nils André Treiber:
A Framework for Data Mining in Wind Power Time Series. DARE 2014: 97-107 - 2013
- [j7]Oliver Kramer, Fabian Gieseke, Kai Lars Polsterer:
Learning morphological maps of galaxies with unsupervised regression. Expert Syst. Appl. 40(8): 2841-2844 (2013) - [j6]Oliver Kramer, Fabian Gieseke, Benjamin Satzger:
Wind energy prediction and monitoring with neural computation. Neurocomputing 109: 84-93 (2013) - [j5]Fabian Gieseke:
From Supervised to Unsupervised Support Vector Machines and Applications in Astronomy. Künstliche Intell. 27(3): 281-285 (2013) - [c13]Fabian Gieseke, Tapio Pahikkala, Christian Igel:
Polynomial Runtime Bounds for Fixed-Rank Unsupervised Least-Squares Classification. ACML 2013: 62-71 - [c12]Oliver Kramer, Nils André Treiber, Fabian Gieseke:
Support Vector Machines for Wind Energy Prediction in Smart Grids. EnviroInfo 2013: 16-24 - [c11]Fabian Gieseke, Oliver Kramer:
Towards Non-linear Constraint Estimation for Expensive Optimization. EvoApplications 2013: 459-468 - [c10]Justin Heinermann, Oliver Kramer, Kai Lars Polsterer, Fabian Gieseke:
On GPU-Based Nearest Neighbor Queries for Large-Scale Photometric Catalogs in Astronomy. KI 2013: 86-97 - 2012
- [j4]Oliver Kramer, Fabian Gieseke:
Evolutionary kernel density regression. Expert Syst. Appl. 39(10): 9246-9254 (2012) - [j3]Fabian Gieseke, Oliver Kramer, Antti Airola, Tapio Pahikkala:
Efficient recurrent local search strategies for semi- and unsupervised regularized least-squares classification. Evol. Intell. 5(3): 189-205 (2012) - [j2]Fabian Gieseke, Gabriel Moruz, Jan Vahrenhold:
Resilient k-d trees: k-means in space revisited. Frontiers Comput. Sci. 6(2): 166-178 (2012) - [c9]Tapio Pahikkala, Antti Airola, Fabian Gieseke, Oliver Kramer:
Unsupervised Multi-class Regularized Least-Squares Classification. ICDM 2012: 585-594 - [c8]Fabian Gieseke, Antti Airola, Tapio Pahikkala, Oliver Kramer:
Sparse Quasi-Newton Optimization for Semi-supervised Support Vector Machines. ICPRAM (1) 2012: 45-54 - [p1]Fabian Gieseke:
Von überwachten zu unüberwachten Support-Vektor-Maschinen und Anwendungen in der Astronomie. Ausgezeichnete Informatikdissertationen 2012: 111-120 - 2011
- [b1]Fabian Gieseke:
From supervised to unsupervised support vector machines and applications in astronomy. Carl von Ossietzky University of Oldenburg, 2011, pp. 1-191 - [c7]Oliver Kramer, Fabian Gieseke:
Analysis of wind energy time series with kernel methods and neural networks. ICNC 2011: 2381-2385 - [c6]Fabian Gieseke, Oliver Kramer, Antti Airola, Tapio Pahikkala:
Speedy Local Search for Semi-Supervised Regularized Least-Squares. KI 2011: 87-98 - [c5]Oliver Kramer, Fabian Gieseke:
Variance Scaling for EDAs Revisited. KI 2011: 169-178 - [c4]Oliver Kramer, Fabian Gieseke:
Short-Term Wind Energy Forecasting Using Support Vector Regression. SOCO 2011: 271-280 - 2010
- [j1]Fabian Gieseke, Joachim Gudmundsson, Jan Vahrenhold:
Pruning spanners and constructing well-separated pair decompositions in the presence of memory hierarchies. J. Discrete Algorithms 8(3): 259-272 (2010) - [c3]Fabian Gieseke, Gabriel Moruz, Jan Vahrenhold:
Resilient K-d Trees: K-Means in Space Revisited. ICDM 2010: 815-820 - [c2]Fabian Gieseke, Kai Lars Polsterer, Andreas Thom, Peter Zinn, Dominik Bomanns, Ralf-Jurgen Dettmar, Oliver Kramer, Jan Vahrenhold:
Detecting Quasars in Large-Scale Astronomical Surveys. ICMLA 2010: 352-357
2000 – 2009
- 2009
- [c1]Fabian Gieseke, Tapio Pahikkala, Oliver Kramer:
Fast evolutionary maximum margin clustering. ICML 2009: 361-368
Coauthor Index
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last updated on 2024-10-07 21:21 CEST by the dblp team
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