Andreas Christian Mueller
Titel
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Geciteerd door
Jaar
Evaluation of pooling operations in convolutional architectures for object recognition
D Scherer, A Müller, S Behnke
Artificial Neural Networks–ICANN 2010, 92-101, 2010
11392010
API design for machine learning software: experiences from the scikit-learn project
L Buitinck, G Louppe, M Blondel, F Pedregosa, A Mueller, O Grisel, ...
arXiv preprint arXiv:1309.0238, 2013
8412013
Machine learning for neuroimaging with scikit-learn
A Abraham, F Pedregosa, M Eickenberg, P Gervais, A Mueller, J Kossaifi, ...
Frontiers in neuroinformatics 8, 14, 2014
4982014
Introduction to machine learning with Python: a guide for data scientists
AC Müller, S Guido
" O'Reilly Media, Inc.", 2016
4462016
Scikit-learn: Machine learning without learning the machinery
G Varoquaux, L Buitinck, G Louppe, O Grisel, F Pedregosa, A Mueller
GetMobile: Mobile Computing and Communications 19 (1), 29-33, 2015
912015
Learning depth-sensitive conditional random fields for semantic segmentation of RGB-D images
AC Müller, S Behnke
2014 IEEE International Conference on Robotics and Automation (ICRA), 6232-6237, 2014
782014
Pystruct: learning structured prediction in python
AC Müller, S Behnke
The Journal of Machine Learning Research 15 (1), 2055-2060, 2014
752014
Investigating convergence of restricted boltzmann machine learning
H Schulz, A Müller, S Behnke
NIPS 2010 Workshop on Deep Learning and Unsupervised Feature Learning 1 (2), 6.1, 2010
482010
Information Theoretic Clustering Using Minimum Spanning Trees
A Müller, S Nowozin, C Lampert
Pattern Recognition, 205-215, 2012
392012
Using machine learning to explore the long-term evolution of GRS 1915+ 105
D Huppenkothen, LM Heil, DW Hogg, A Mueller
Monthly Notices of the Royal Astronomical Society 466 (2), 2364-2377, 2017
132017
Einführung in Machine learning mit Python: Praxiswissen data science
AC Müller, S Guido
O'Reilly, 2017
92017
Exploiting local structure in Boltzmann machines
H Schulz, A Müller, S Behnke
Neurocomputing 74 (9), 1411-1417, 2011
82011
Learning multiple defaults for machine learning algorithms
F Pfisterer, JN van Rijn, P Probst, A Müller, B Bischl
arXiv preprint arXiv:1811.09409, 2018
72018
Methods for learning structured prediction in semantic segmentation of natural images
A Müller
Universitäts-und Landesbibliothek Bonn, 2014
72014
Multi-instance methods for partially supervised image segmentation
A Müller, S Behnke
IAPR International Workshop on Partially Supervised Learning, 110-119, 2011
62011
Importance of tuning hyperparameters of machine learning algorithms
HJP Weerts, AC Mueller, J Vanschoren
arXiv preprint arXiv:2007.07588, 2020
52020
Meta Learning for Defaults–Symbolic Defaults
JN van Rijn, F Pfisterer, J Thomas, A Muller, B Bischl, J Vanschoren
Neural Information Processing Workshop on Meta-Learning, 2018
52018
Learning a Loopy Model For Semantic Segmentation Exactly
AC Müller, S Behnke
arXiv preprint arXiv:1309.4061, 2013
52013
Topological features in locally connected RBMs
A Müller, H Schulz, S Behnke
The 2010 International Joint Conference on Neural Networks (IJCNN), 1-6, 2010
52010
Machine learning avec Python
AC Müller, S Guido
First interactive, 2018
42018
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Artikelen 1–20