Следене
Frank Hutter
Frank Hutter
Professor of Computer Science, University of Freiburg, Germany
Потвърден имейл адрес: cs.uni-freiburg.de - Начална страница
Заглавие
Позовавания
Позовавания
Година
Decoupled weight decay regularization
I Loshchilov, F Hutter
arXiv preprint arXiv:1711.05101, 2017
4379*2017
Sgdr: Stochastic gradient descent with warm restarts
I Loshchilov, F Hutter
arXiv preprint arXiv:1608.03983, 2016
32772016
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
23432011
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
17612015
Neural architecture search: A survey
T Elsken, JH Metzen, F Hutter
The Journal of Machine Learning Research 20 (1), 1997-2017, 2019
16242019
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
14202013
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
13432017
ParamILS: an automatic algorithm configuration framework
F Hutter, HH Hoos, K Leyton-Brown, T Stützle
Journal of Artificial Intelligence Research 36, 267-306, 2009
10892009
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
9802008
Automated machine learning: methods, systems, challenges
F Hutter, L Kotthoff, J Vanschoren
Springer Nature, 2019
8862019
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
6622019
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
6622019
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
6552017
BOHB: Robust and efficient hyperparameter optimization at scale
S Falkner, A Klein, F Hutter
International Conference on Machine Learning, 1437-1446, 2018
6412018
Hyperparameter optimization
M Feurer, F Hutter
Automated machine learning, 3-33, 2019
5882019
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
5532015
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
4762017
Algorithm runtime prediction: Methods & evaluation
F Hutter, L Xu, HH Hoos, K Leyton-Brown
Artificial Intelligence 206, 79-111, 2014
4602014
Initializing Bayesian Hyperparameter Optimization via Meta-Learning.
M Feurer, JT Springenberg, F Hutter
AAAI, 1128-1135, 2015
441*2015
Automatic algorithm configuration based on local search
F Hutter, HH Hoos, T Stützle
Aaai 7, 1152-1157, 2007
3732007
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