| 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 | 3020 | 2013 |
| Evaluation of pooling operations in convolutional architectures for object recognition D Scherer, A Müller, S Behnke Artificial Neural Networks–ICANN 2010, 92-101, 2010 | 2126 | 2010 |
| Introduction to machine learning with Python: a guide for data scientists AC Müller, S Guido " O'Reilly Media, Inc.", 2016 | 1743 | 2016 |
| 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 | 1613 | 2014 |
| 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 | 535 | 2015 |
| Importance of tuning hyperparameters of machine learning algorithms HJP Weerts, AC Mueller, J Vanschoren arXiv preprint arXiv:2007.07588, 2020 | 111 | 2020 |
| PyStruct: learning structured prediction in python. AC Müller, S Behnke J. Mach. Learn. Res. 15 (1), 2055-2060, 2014 | 100 | 2014 |
| 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 | 94 | 2014 |
| 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 | 71 | 2021 |
| Information Theoretic Clustering Using Minimum Spanning Trees A Müller, S Nowozin, C Lampert Pattern Recognition, 205-215, 2012 | 64 | 2012 |
| 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 | 63 | 2010 |
| 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 | |
| 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 | 34 | 2017 |
| 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 | 33 | 2021 |
| 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 | 26 | 2021 |
| 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 | 20 | 2019 |
| scikit-learn/scikit-learn: Scikit-learn 0.22. 1 O Grisel, A Mueller, A Gramfort, G Louppe, P Prettenhofer, M Blondel, ... Zenodo, 2020 | 12 | 2020 |
| LlamaTune: sample-efficient DBMS configuration tuning K Kanellis, C Ding, B Kroth, A Müller, C Curino, S Venkataraman arXiv preprint arXiv:2203.05128, 2022 | 10 | 2022 |
| 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 | 10 | 2018 |
| Exploiting local structure in Boltzmann machines H Schulz, A Müller, S Behnke Neurocomputing 74 (9), 1411-1417, 2011 | 10 | 2011 |
Sven BehnkeProfessor for Autonomous Intelligent Systems, Computer Science Institute, University of BonnVerified email at cs.uni-bonn.de
