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Frank Hutter
Frank Hutter
Professor of Computer Science, University of Freiburg, Germany
Verified email at cs.uni-freiburg.de - Homepage
Title
Cited by
Cited by
Year
Decoupled weight decay regularization
I Loshchilov, F Hutter
arXiv preprint arXiv:1711.05101, 2017
4813*2017
Sgdr: Stochastic gradient descent with warm restarts
I Loshchilov, F Hutter
arXiv preprint arXiv:1608.03983, 2016
34712016
Sequential model-based optimization for general algorithm configuration
F Hutter, HH Hoos, K Leyton-Brown
International conference on learning and intelligent optimization, 507-523, 2011
23952011
Efficient and robust automated machine learning
M Feurer, A Klein, K Eggensperger, J Springenberg, M Blum, F Hutter
Advances in neural information processing systems 28, 2015
18272015
Neural architecture search: A survey
T Elsken, JH Metzen, F Hutter
The Journal of Machine Learning Research 20 (1), 1997-2017, 2019
16832019
Auto-WEKA: Combined selection and hyperparameter optimization of classification algorithms
C Thornton, F Hutter, HH Hoos, K Leyton-Brown
Proceedings of the 19th ACM SIGKDD international conference on Knowledge …, 2013
14482013
Deep learning with convolutional neural networks for EEG decoding and visualization
RT Schirrmeister, JT Springenberg, LDJ Fiederer, M Glasstetter, ...
Human brain mapping 38 (11), 5391-5420, 2017
14062017
ParamILS: an automatic algorithm configuration framework
F Hutter, HH Hoos, K Leyton-Brown, T Stützle
Journal of Artificial Intelligence Research 36, 267-306, 2009
10982009
SATzilla: portfolio-based algorithm selection for SAT
L Xu, F Hutter, HH Hoos, K Leyton-Brown
Journal of artificial intelligence research 32, 565-606, 2008
9882008
Automated machine learning: methods, systems, challenges
F Hutter, L Kotthoff, J Vanschoren
Springer Nature, 2019
9292019
Auto-WEKA: Automatic model selection and hyperparameter optimization in WEKA
L Kotthoff, C Thornton, HH Hoos, F Hutter, K Leyton-Brown
Automated Machine Learning, 81-95, 2019
6732019
Auto-WEKA: Automatic model selection and hyperparameter optimization in WEKA
L Kotthoff, C Thornton, HH Hoos, F Hutter, K Leyton-Brown
Automated Machine Learning, 81-95, 2019
6732019
BOHB: Robust and efficient hyperparameter optimization at scale
S Falkner, A Klein, F Hutter
International Conference on Machine Learning, 1437-1446, 2018
6662018
Auto-WEKA 2.0: Automatic model selection and hyperparameter optimization in WEKA
L Kotthoff, C Thornton, HH Hoos, F Hutter, K Leyton-Brown
Journal of Machine Learning Research 18 (25), 1-5, 2017
6662017
Hyperparameter optimization
M Feurer, F Hutter
Automated machine learning, 3-33, 2019
6202019
Speeding up automatic hyperparameter optimization of deep neural networks by extrapolation of learning curves
T Domhan, JT Springenberg, F Hutter
Twenty-fourth international joint conference on artificial intelligence, 2015
5652015
Fast bayesian optimization of machine learning hyperparameters on large datasets
A Klein, S Falkner, S Bartels, P Hennig, F Hutter
Artificial intelligence and statistics, 528-536, 2017
4832017
Algorithm runtime prediction: Methods & evaluation
F Hutter, L Xu, HH Hoos, K Leyton-Brown
Artificial Intelligence 206, 79-111, 2014
4692014
Initializing Bayesian Hyperparameter Optimization via Meta-Learning.
M Feurer, JT Springenberg, F Hutter
AAAI, 1128-1135, 2015
448*2015
Efficient multi-objective neural architecture search via lamarckian evolution
T Elsken, JH Metzen, F Hutter
arXiv preprint arXiv:1804.09081, 2018
3802018
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