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Frank Hutter
Frank Hutter
Professor of Computer Science, University of Freiburg, Germany
Bestätigte E-Mail-Adresse bei cs.uni-freiburg.de - Startseite
Titel
Zitiert von
Zitiert von
Jahr
Decoupled weight decay regularization
I Loshchilov, F Hutter
arXiv preprint arXiv:1711.05101, 2017
7470*2017
Sgdr: Stochastic gradient descent with warm restarts
I Loshchilov, F Hutter
arXiv preprint arXiv:1608.03983, 2016
45712016
Sequential model-based optimization for general algorithm configuration
F Hutter, HH Hoos, K Leyton-Brown
Learning and Intelligent Optimization: 5th International Conference, LION 5 …, 2011
25992011
Neural architecture search: A survey
T Elsken, JH Metzen, F Hutter
The Journal of Machine Learning Research 20 (1), 1997-2017, 2019
20702019
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
20612015
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
16962017
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
15832013
Automated machine learning: methods, systems, challenges
F Hutter, L Kotthoff, J Vanschoren
Springer Nature, 2019
11692019
ParamILS: an automatic algorithm configuration framework
F Hutter, HH Hoos, K Leyton-Brown, T Stützle
Journal of Artificial Intelligence Research 36, 267-306, 2009
11462009
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
10382008
Hyperparameter optimization
M Feurer, F Hutter
Automated machine learning: Methods, systems, challenges, 3-33, 2019
8382019
BOHB: Robust and efficient hyperparameter optimization at scale
S Falkner, A Klein, F Hutter
International Conference on Machine Learning, 1437-1446, 2018
8082018
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
7482019
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
7482019
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
7482017
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
6362015
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
5412017
Algorithm runtime prediction: Methods & evaluation
F Hutter, L Xu, HH Hoos, K Leyton-Brown
Artificial Intelligence 206, 79-111, 2014
5002014
Initializing Bayesian Hyperparameter Optimization via Meta-Learning.
M Feurer, JT Springenberg, F Hutter
AAAI, 1128-1135, 2015
495*2015
Nas-bench-101: Towards reproducible neural architecture search
C Ying, A Klein, E Christiansen, E Real, K Murphy, F Hutter
International Conference on Machine Learning, 7105-7114, 2019
4682019
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