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Fabian Gieseke
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2020 – today
- 2024
- [j17]Denis Mayr Lima Martins
, Christian Lülf
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
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
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
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
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
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
Deep learning based 3D point cloud regression for estimating forest biomass. SIGSPATIAL/GIS 2022: 38:1-38:4 - [c36]Stefan Oehmcke
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
Estimating Forest Canopy Height With Multi-Spectral and Multi-Temporal Imagery Using Deep Learning. IEEE BigData 2021: 4915-4924 - [c34]Philip Munksgaard
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
Attentional Feature Fusion. WACV 2021: 3559-3568 - [i11]Stefan Oehmcke
Deep Learning Based 3D Point Cloud Regression for Estimating Forest Biomass. CoRR abs/2112.11335 (2021) - 2020
- [j14]Eliakim Hamunyela, Sabina Rosca, Andrei Mîrt
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
Approximate Nearest-Neighbour Fields via Massively-Parallel Propagation-Assisted K-D Trees. IEEE BigData 2020: 5172-5181 - [c31]Stefan Oehmcke
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
Massively-Parallel Change Detection for Satellite Time Series Data with Missing Values. ICDE 2020: 385-396 - [c29]Yimian Dai, Stefan Oehmcke
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
Magnitude and Uncertainty Pruning Criterion for Neural Networks. IEEE BigData 2019: 2317-2326 - [c27]Stefan Oehmcke, Christoffer Thrysøe, Andreas Borgstad, Marcos Antonio Vaz Salles
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
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
Big Universe, Big Data: Machine Learning and Image Analysis for Astronomy. IEEE Intell. Syst. 32(2): 16-22 (2017) - [j11]Fabian Gieseke
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
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
Nearest neighbor density ratio estimation for large-scale applications in astronomy. Astron. Comput. 12: 67-72 (2015) - [c18]Fabian Gieseke, Tapio Pahikkala
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
Fast and simple gradient-based optimization for semi-supervised support vector machines. Neurocomputing 123: 23-32 (2014) - [j8]Tapio Pahikkala
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
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
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
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
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
Detecting Quasars in Large-Scale Astronomical Surveys. ICMLA 2010: 352-357
2000 – 2009
- 2009
- [c1]Fabian Gieseke, Tapio Pahikkala
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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