Frank Hutter

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	Toate	Din 2019
Referințe bibliografice	77657	69220
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Holger HoosRWTH Aachen University, Germany • Leiden University, Netherlands • University of British ColumbiaAdresă de e-mail confirmată pe cs.rwth-aachen.de
Kevin Leyton-BrownProfessor, Computer Science, University of British Columbia; Canada CIFAR AI ChairAdresă de e-mail confirmată pe cs.ubc.ca
Marius LindauerLeibniz University Hannover (Germany), Institute of Artificial Intelligence LUH|AI, L3S ResearchAdresă de e-mail confirmată pe ai.uni-hannover.de
Katharina EggenspergerEarly Career Group Leader "AutoML for Science" | University of TübingenAdresă de e-mail confirmată pe uni-tuebingen.de
Ilya LoshchilovNVIDIAAdresă de e-mail confirmată pe nvidia.com
Matthias FeurerLMU MunichAdresă de e-mail confirmată pe stat.uni-muenchen.de
Aaron KleinScaDS.AIAdresă de e-mail confirmată pe amazon.com
Stefan FalknerBosch Corporate ResearchAdresă de e-mail confirmată pe de.bosch.com
Lin XuNETINT, University of British ColumbiaAdresă de e-mail confirmată pe cs.ubc.ca
Tonio BallNeuromedical AI Lab Uni Freiburg, NeuroMentum AI (CEO)Adresă de e-mail confirmată pe uniklinik-freiburg.de
Joaquin VanschorenEindhoven University of TechnologyAdresă de e-mail confirmată pe tue.nl
Lars KotthoffUniversity of WyomingAdresă de e-mail confirmată pe uwyo.edu
Wolfram BurgardProfessor of Computer Science, Technical University of NurembergAdresă de e-mail confirmată pe utn.de
Michael TangermannDonders Insitute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands, andAdresă de e-mail confirmată pe donders.ru.nl
Bernd BischlChair of Statistical Learning and Data Science, LMU MunichAdresă de e-mail confirmată pe stat.uni-muenchen.de
Thomas StützleF.R.S.-FNRS Research Director, Université Libre de Bruxelles (ULB)Adresă de e-mail confirmată pe ulb.ac.be
Kevin MurphyResearch Scientist, GoogleAdresă de e-mail confirmată pe google.com
Manuel BlumUniversity of FreiburgAdresă de e-mail confirmată pe informatik.uni-freiburg.de
Nando de FreitasCIFAR & DeepMindAdresă de e-mail confirmată pe google.com
Philipp HennigUniversity of TübingenAdresă de e-mail confirmată pe uni-tuebingen.de

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Frank Hutter

ELLIS Institute & Professor, University of Freiburg, Germany

Adresă de e-mail confirmată pe cs.uni-freiburg.de - Pagina de pornire

AutoML Meta-Learning Neural Architecture Search Deep Learning Machine Learning


Titlu Sortați după descrierea bibliografică Sortați după an Sortați după titlu	Citat de Citat de	Anul
Decoupled weight decay regularization I Loshchilov, F Hutter ICLR 2019, 2017	21772	2017
Sgdr: Stochastic gradient descent with warm restarts I Loshchilov, F Hutter ICLR 2017, 2016	8656	2016
Neural architecture search: A survey T Elsken, JH Metzen, F Hutter Journal of Machine Learning Research 20 (55), 1-21, 2019	3322	2019
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	3277	2011
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	2865	2015
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	2760	2017
Automated machine learning: methods, systems, challenges F Hutter, L Kotthoff, J Vanschoren Springer Nature, 2019	2052	2019
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	2049	2013
Hyperparameter optimization M Feurer, F Hutter Automated machine learning: Methods, systems, challenges, 3-33, 2019	1630	2019
ParamILS: an automatic algorithm configuration framework F Hutter, HH Hoos, K Leyton-Brown, T Stützle Journal of artificial intelligence research 36, 267-306, 2009	1275	2009
BOHB: Robust and efficient hyperparameter optimization at scale S Falkner, A Klein, F Hutter International conference on machine learning, 1437-1446, 2018	1267	2018
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	1157	2008
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	965	2017
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	840	2016
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	787	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	786	2019
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	730	2017
A downsampled variant of imagenet as an alternative to the cifar datasets P Chrabaszcz, I Loshchilov, F Hutter arXiv preprint arXiv:1707.08819, 2017	656	2017
Efficient multi-objective neural architecture search via lamarckian evolution T Elsken, JH Metzen, F Hutter arXiv preprint arXiv:1804.09081, 2018	626	2018
An efficient approach for assessing hyperparameter importance F Hutter, H Hoos, K Leyton-Brown International conference on machine learning, 754-762, 2014	609	2014

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