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Andreas Christian Mueller
Andreas Christian Mueller
Microsoft
Bestätigte E-Mail-Adresse bei microsoft.com - Startseite
Titel
Zitiert von
Zitiert von
Jahr
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
30242013
Evaluation of pooling operations in convolutional architectures for object recognition
D Scherer, A Müller, S Behnke
Artificial Neural Networks–ICANN 2010, 92-101, 2010
21252010
Introduction to machine learning with Python: a guide for data scientists
AC Müller, S Guido
" O'Reilly Media, Inc.", 2016
17502016
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
16152014
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
5372015
Importance of tuning hyperparameters of machine learning algorithms
HJP Weerts, AC Mueller, J Vanschoren
arXiv preprint arXiv:2007.07588, 2020
1112020
PyStruct: learning structured prediction in python.
AC Müller, S Behnke
J. Mach. Learn. Res. 15 (1), 2055-2060, 2014
1002014
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
942014
Openml-python: an extensible python api for openml
M Feurer, JN Van Rijn, A Kadra, P Gijsbers, N Mallik, S Ravi, A Müller, ...
The Journal of Machine Learning Research 22 (1), 4573-4577, 2021
712021
Information Theoretic Clustering Using Minimum Spanning Trees
A Müller, S Nowozin, C Lampert
Pattern Recognition, 205-215, 2012
642012
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
632010
Scikit-learn: Machine Learning in Python. arXiv 2012
F Pedregosa, G Varoquaux, A Gramfort, V Michel, B Thirion, O Grisel, ...
arXiv preprint arXiv:1201.0490, 0
61
Learning multiple defaults for machine learning algorithms
F Pfisterer, JN van Rijn, P Probst, AC Müller, B Bischl
Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2021
332021
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
332017
Magpie: Python at Speed and Scale using Cloud Backends.
A Jindal, KV Emani, M Daum, O Poppe, B Haynes, A Pavlenko, A Gupta, ...
CIDR, 2021
262021
Hyperparameter importance for image classification by residual neural networks
A Sharma, JN van Rijn, F Hutter, A Müller
Discovery Science: 22nd International Conference, DS 2019, Split, Croatia …, 2019
202019
scikit-learn/scikit-learn: Scikit-learn 0.22. 1
O Grisel, A Mueller, A Gramfort, G Louppe, P Prettenhofer, M Blondel, ...
Zenodo, 2020
122020
LlamaTune: sample-efficient DBMS configuration tuning
K Kanellis, C Ding, B Kroth, A Müller, C Curino, S Venkataraman
arXiv preprint arXiv:2203.05128, 2022
102022
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
102018
Exploiting local structure in Boltzmann machines
H Schulz, A Müller, S Behnke
Neurocomputing 74 (9), 1411-1417, 2011
102011
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