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Frank Hutter
Frank Hutter
ELLIS Institute Tübingen & Professor, University of Freiburg, Germany
Zweryfikowany adres z cs.uni-freiburg.de - Strona główna
Tytuł
Cytowane przez
Cytowane przez
Rok
Decoupled weight decay regularization
I Loshchilov, F Hutter
ICLR 2019, 2017
246242017
Sgdr: Stochastic gradient descent with warm restarts
I Loshchilov, F Hutter
ICLR 2017, 2016
93712016
Neural architecture search: A survey
T Elsken, JH Metzen, F Hutter
Journal of Machine Learning Research 20 (55), 1-21, 2019
34902019
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
33752011
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
29732017
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
29572015
Automated machine learning: methods, systems, challenges
F Hutter, L Kotthoff, J Vanschoren
Springer Nature, 2019
22062019
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
21122013
Hyperparameter optimization
M Feurer, F Hutter
Automated machine learning: Methods, systems, challenges, 3-33, 2019
17442019
BOHB: Robust and efficient hyperparameter optimization at scale
S Falkner, A Klein, F Hutter
International conference on machine learning, 1437-1446, 2018
13472018
ParamILS: an automatic algorithm configuration framework
F Hutter, HH Hoos, K Leyton-Brown, T Stützle
Journal of artificial intelligence research 36, 267-306, 2009
12892009
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
11792008
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
9852017
Bayesian optimization in a billion dimensions via random embeddings
Z Wang, F Hutter, M Zoghi, D Matheson, N De Feitas
Journal of Artificial Intelligence Research 55, 361-387, 2016
8712016
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
8312019
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
8112015
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
7462017
A downsampled variant of imagenet as an alternative to the cifar datasets
P Chrabaszcz, I Loshchilov, F Hutter
arXiv preprint arXiv:1707.08819, 2017
6962017
Efficient multi-objective neural architecture search via lamarckian evolution
T Elsken, JH Metzen, F Hutter
arXiv preprint arXiv:1804.09081, 2018
6542018
An efficient approach for assessing hyperparameter importance
F Hutter, H Hoos, K Leyton-Brown
International conference on machine learning, 754-762, 2014
6382014
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