Fabian Gieseke

发表

Learning Selection Masks for Deep Neural Networks pdf

Stefan Oehmcke, Fabian Gieseke, 2019, ArXiv.

On GPU-Based Nearest Neighbor Queries for Large-Scale Photometric Catalogs in Astronomy

Oliver Kramer, Fabian Gieseke, Justin Heinermann, 2013, KI.

Exploring the spectroscopic diversity of type Ia supernovae with Deep Learning and Unsupervised Clustering

Ricardo Vilalta, Fabian Gieseke, Emille E. O. Ishida, 2016, Proceedings of the International Astronomical Union.

Analyzing Complex and Structured Data via Unsupervised Learning Techniques

Fabian Gieseke, Kai Lars Polsterer, Dennis Kügler, 2015 .

PINK: Parallelized rotation and flipping INvariant Kohonen maps

Fabian Gieseke, Kai Lars Polsterer, Bernd Doser, 2019 .

Learning morphological maps of galaxies with unsupervised regression

Oliver Kramer, Fabian Gieseke, Kai Lars Polsterer, 2013, Expert Syst. Appl..

Deep-learnt classification of light curves

Akshay Pai, Fabian Gieseke, Matthew J. Graham, 2017, 2017 IEEE Symposium Series on Computational Intelligence (SSCI).

An Efficient Many-Core Implementation for Semi-Supervised Support Vector Machines

Fabian Gieseke, F. Gieseke, 2015, MOD.

Massively-parallel break detection for satellite data

Achim Zeileis, Jan Verbesselt, Fabian Gieseke, 2018, SSDBM.

A Framework for Data Mining in Wind Power Time Series

Oliver Kramer, Fabian Gieseke, Justin Heinermann, 2014, DARE.

Massively-Parallel Change Detection for Satellite Time Series Data with Missing Values

Jan Verbesselt, Fabian Gieseke, Troels Henriksen, 2020, 2020 IEEE 36th International Conference on Data Engineering (ICDE).

Buffer k-d Trees: Processing Massive Nearest Neighbor Queries on GPUs

Fabian Gieseke, Christian Igel, Justin Heinermann, 2014, ICML.

Big Universe, Big Data: Machine Learning and Image Analysis for Astronomy

Christian Igel, Fabian Gieseke, Kim Steenstrup Pedersen, 2017, IEEE Intelligent Systems.

Approximate Nearest-Neighbour Fields via Massively-Parallel Propagation-Assisted K-D Trees

Fabian Gieseke, Cosmin E. Oancea, Ties Robroek, 2020 .

Implementation of BFASTmonitor Algorithm on Google Earth Engine to Support Large-Area and Sub-Annual Change Monitoring Using Earth Observation Data

Fabian Gieseke, Jan Verbesselt, Eliakim Hamunyela, 2020, Remote. Sens..

Fast and simple gradient-based optimization for semi-supervised support vector machines

Oliver Kramer, Tapio Pahikkala, Antti Airola, 2014, Neurocomputing.

Bigger Buffer k-d Trees on Multi-Many-Core Systems

Tom Heskes, Fabian Gieseke, Christian Igel, 2018, VECPAR.

Detecting Quasars in Large-Scale Astronomical Surveys

Oliver Kramer, Jan Vahrenhold, Fabian Gieseke, 2010, 2010 Ninth International Conference on Machine Learning and Applications.

Machine Learning in Wind Energy Information Systems.

Oliver Kramer, Fabian Gieseke, Nils André Treiber, 2013 .

Artistic Movement Recognition by Boosted Fusion of Color Structure and Topographic Description

Fabian Gieseke, Corneliu Florea, Cosmin Toca, 2017, 2017 IEEE Winter Conference on Applications of Computer Vision (WACV).

From Supervised to Unsupervised Support Vector Machines and Applications in Astronomy

Fabian Gieseke, F. Gieseke, 2013, KI - Künstliche Intelligenz.

Automatic Galaxy Classification via Machine Learning Techniques: Parallelized Rotation/Flipping INvariant Kohonen Maps (PINK)

Christian Igel, Fabian Gieseke, Christian Igel, 2015 .

Polynomial Runtime Bounds for Fixed-Rank Unsupervised Least-Squares Classification

Tapio Pahikkala, Christian Igel, Fabian Gieseke, 2013, ACML.

Towards Non-linear Constraint Estimation for Expensive Optimization

Oliver Kramer, Fabian Gieseke, F. Gieseke, 2013, EvoApplications.

Short-Term Wind Energy Forecasting Using Support Vector Regression

Oliver Kramer, Fabian Gieseke, F. Gieseke, 2011, SOCO.

Massively-parallel best subset selection for ordinary least-squares regression

Tom Heskes, Fabian Gieseke, Christian Igel, 2017, 2017 IEEE Symposium Series on Computational Intelligence (SSCI).

Artistic movement recognition by consensus of boosted SVM based experts

Fabian Gieseke, Corneliu Florea, F. Gieseke, 2018, J. Vis. Commun. Image Represent..

Analysis of wind energy time series with kernel methods and neural networks

Oliver Kramer, Fabian Gieseke, F. Gieseke, 2011, 2011 Seventh International Conference on Natural Computation.

Resilient K-d Trees: K-Means in Space Revisited

Jan Vahrenhold, Fabian Gieseke, Gabriel Moruz, 2010, ICDM.

Speedy Local Search for Semi-Supervised Regularized Least-Squares

Oliver Kramer, Tapio Pahikkala, Antti Airola, 2011, KI.

Speedy greedy feature selection: Better redshift estimation via massive parallelism

Fabian Gieseke, Christian Igel, Kai Lars Polsterer, 2014, ESANN.

bufferkdtree: A Python library for massive nearest neighbor queries on multi-many-core devices

Fabian Gieseke, Christian Igel, Cosmin E. Oancea, 2017, Knowl. Based Syst..

Pruning spanners and constructing well-separated pair decompositions in the presence of memory hierarchies

Joachim Gudmundsson, Jan Vahrenhold, Fabian Gieseke, 2010, J. Discrete Algorithms.

Creating cloud-free satellite imagery from image time series with deep learning

Stefan Oehmcke, Fabian Gieseke, Alexander V. Prishchepov, 2020, BigSpatial@SIGSPATIAL.

Inferring astrophysical X-ray polarization with deep learning pdf

Fabian Gieseke, Nikita Moriakov, Luc Hendriks, 2020, ArXiv.

Magnitude and Uncertainty Pruning Criterion for Neural Networks

Stefan Oehmcke, Fabian Gieseke, Vinnie Ko, 2019, 2019 IEEE International Conference on Big Data (Big Data).

Fast evolutionary maximum margin clustering

Oliver Kramer, Tapio Pahikkala, Fabian Gieseke, 2009, ICML '09.

Efficient recurrent local search strategies for semi- and unsupervised regularized least-squares classification

Oliver Kramer, Tapio Pahikkala, Antti Airola, 2012, Evol. Intell..

Variance Scaling for EDAs Revisited

Oliver Kramer, Fabian Gieseke, F. Gieseke, 2011, KI.

Improving the performance of photometric regression models via massive parallel feature selection

Christian Igel, Fabian Gieseke, Kai Lars Polsterer, 2014 .

Wind energy prediction and monitoring with neural computation

Oliver Kramer, Fabian Gieseke, Benjamin Satzger, 2013, Neurocomputing.

Deep-Learning the Time Domain

Fabian Gieseke, Ashish A. Mahabal, Andrew J. Drake, 2017, Proceedings of the International Astronomical Union.

Evolutionary kernel density regression

Oliver Kramer, Fabian Gieseke, F. Gieseke, 2012, Expert Syst. Appl..

On Unsupervised Training of Multi-Class Regularized Least-Squares Classifiers

Oliver Kramer, Tapio Pahikkala, Antti Airola, 2014, Journal of Computer Science and Technology.

Detecting Hardly Visible Roads in Low-Resolution Satellite Time Series Data

Stefan Oehmcke, Fabian Gieseke, Marcos Antonio Vaz Salles, 2019, 2019 IEEE International Conference on Big Data (Big Data).

End-To-End Input Selection for Deep Neural Networks

Stefan Oehmcke, Fabian Gieseke, 2019 .

Sparse Quasi-Newton Optimization for Semi-supervised Support Vector Machines

Oliver Kramer, Tapio Pahikkala, Antti Airola, 2012, ICPRAM.

Galaxy Classification without Feature Extraction

Fabian Gieseke, Oliver Kramer, Kai Lars Polsterer, 2012 .

Parallelized rotation and flipping INvariant Kohonen maps (PINK) on GPUs

Fabian Gieseke, Christian Igel, Kai Lars Polsterer, 2016, ESANN.

Training Big Random Forests with Little Resources

Fabian Gieseke, Christian Igel, C. Igel, 2018, KDD.

Unsupervised Multi-class Regularized Least-Squares Classification

Oliver Kramer, Tapio Pahikkala, Antti Airola, 2012, 2012 IEEE 12th International Conference on Data Mining.

Support Vector Machines for Wind Energy Prediction in Smart Grids

Oliver Kramer, Fabian Gieseke, Nils André Treiber, 2013, EnviroInfo.

Attentional Feature Fusion pdf

Stefan Oehmcke, Fabian Gieseke, Yimian Dai, 2020, 2021 IEEE Winter Conference on Applications of Computer Vision (WACV).

Von überwachten zu unüberwachten Support-Vektor-Maschinen und Anwendungen in der Astronomie

Fabian Gieseke, 2012, Ausgezeichnete Informatikdissertationen.

Batch Steepest-Descent-Mildest-Ascent for Interactive Maximum Margin Clustering

Tapio Pahikkala, Tom Heskes, Fabian Gieseke, 2015, IDA.

Resilient k-d trees: k-means in space revisited

Jan Vahrenhold, Fabian Gieseke, Gabriel Moruz, 2010, 2010 IEEE International Conference on Data Mining.

Nearest neighbor density ratio estimation for large-scale applications in astronomy

Fabian Gieseke, Christian Igel, K. S. Pedersen, 2015, Astron. Comput..

Dataset Sensitive Autotuning of Multi-versioned Code Based on Monotonic Properties - Autotuning in Futhark

Fabian Gieseke, Troels Henriksen, Cosmin E. Oancea, 2021, TFP.

Attentional Feature Fusion

Yiquan Wu, Stefan Oehmcke, Fabian Gieseke, 2021, 2021 IEEE Winter Conference on Applications of Computer Vision (WACV).

Deep Learning Based 3D Point Cloud Regression for Estimating Forest Biomass pdf

Stefan Oehmcke, Fabian Gieseke, Christian Igel, 2021, ArXiv.

Convolutional neural networks for transient candidate vetting in large-scale surveys

Brad E. Tucker, Tom Heskes, Fabian Gieseke, 2017, 1708.08947.

Photometric Redshift Estimation of Quasars: Local versus Global Regression

Fabian Gieseke, Kai Polsterer, P. C. Zinn, 2012 .

Simple, Fast and Accurate Photometric Estimation of Specific Star Formation Rate

Fabian Gieseke, Christian Igel, Kim Steenstrup Pedersen, 2015 .

Learning Preferences with Co-Regularized Least-Squares

Tapio Salakoski, Tapio Pahikkala, Fabian Gieseke, 2008 .

An unexpectedly large count of trees in the West African Sahara and Sahel

Martin Brandt, Johannes Schöning, Rasmus Fensholt, 2020, Nature.

Sacrificing information for the greater good: how to select photometric bands for optimal accuracy

Fabian Gieseke, Christian Igel, Kim Steenstrup Pedersen, 2015, Monthly Notices of the Royal Astronomical Society.

Return of the features

Fabian Gieseke, Stefano Cavuoti, Kai Lars Polsterer, 2018, Astronomy & Astrophysics.

Uncertain Photometric Redshifts

Fabian Gieseke, Kai Lars Polsterer, Antonio D'Isanto, 2016 .