Theo dơi
Martin Wistuba
Martin Wistuba
Amazon Web Services
Email được xác minh tại ismll.de
Tiêu đề
Trích dẫn bởi
Trích dẫn bởi
Năm
Adversarial Robustness Toolbox v1. 0.0
MI Nicolae, M Sinn, MN Tran, B Buesser, A Rawat, M Wistuba, ...
arXiv preprint arXiv:1807.01069, 2018
6292018
Learning time-series shapelets
J Grabocka, N Schilling, M Wistuba, L Schmidt-Thieme
Proceedings of the 20th ACM SIGKDD international conference on Knowledge …, 2014
5792014
A survey on neural architecture search
M Wistuba, A Rawat, T Pedapati
arXiv preprint arXiv:1905.01392, 2019
3692019
Scalable gaussian process-based transfer surrogates for hyperparameter optimization
M Wistuba, N Schilling, L Schmidt-Thieme
Machine Learning 107 (1), 43-78, 2018
1422018
Learning hyperparameter optimization initializations
M Wistuba, N Schilling, L Schmidt-Thieme
2015 IEEE international conference on data science and advanced analytics …, 2015
1262015
Ultra-fast shapelets for time series classification
M Wistuba, J Grabocka, L Schmidt-Thieme
arXiv preprint arXiv:1503.05018, 2015
992015
A comprehensive survey on hardware-aware neural architecture search
H Benmeziane, KE Maghraoui, H Ouarnoughi, S Niar, M Wistuba, ...
arXiv preprint arXiv:2101.09336, 2021
952021
Fast classification of univariate and multivariate time series through shapelet discovery
J Grabocka, M Wistuba, L Schmidt-Thieme
Knowledge and information systems 49, 429-454, 2016
882016
Personalized deep learning for tag recommendation
HTH Nguyen, M Wistuba, J Grabocka, LR Drumond, L Schmidt-Thieme
Advances in Knowledge Discovery and Data Mining: 21st Pacific-Asia …, 2017
812017
Two-stage transfer surrogate model for automatic hyperparameter optimization
M Wistuba, N Schilling, L Schmidt-Thieme
Machine Learning and Knowledge Discovery in Databases: European Conference …, 2016
812016
Hyperparameter search space pruning–a new component for sequential model-based hyperparameter optimization
M Wistuba, N Schilling, L Schmidt-Thieme
Machine Learning and Knowledge Discovery in Databases: European Conference …, 2015
812015
Few-shot bayesian optimization with deep kernel surrogates
M Wistuba, J Grabocka
arXiv preprint arXiv:2101.07667, 2021
742021
Deep learning architecture search by neuro-cell-based evolution with function-preserving mutations
M Wistuba
Machine Learning and Knowledge Discovery in Databases: European Conference …, 2019
662019
Sequential model-free hyperparameter tuning
M Wistuba, N Schilling, L Schmidt-Thieme
2015 IEEE international conference on data mining, 1033-1038, 2015
562015
Learning DTW-shapelets for time-series classification
M Shah, J Grabocka, N Schilling, M Wistuba, L Schmidt-Thieme
Proceedings of the 3rd IKDD Conference on Data Science, 2016, 1-8, 2016
552016
Hardware-Aware Neural Architecture Search: Survey and Taxonomy.
H Benmeziane, K El Maghraoui, H Ouarnoughi, S Niar, M Wistuba, ...
IJCAI, 4322-4329, 2021
482021
Optimal exploitation of clustering and history information in multi-armed bandit
D Bouneffouf, S Parthasarathy, H Samulowitz, M Wistub
arXiv preprint arXiv:1906.03979, 2019
472019
Hyperparameter optimization with factorized multilayer perceptrons
N Schilling, M Wistuba, L Drumond, L Schmidt-Thieme
Machine Learning and Knowledge Discovery in Databases: European Conference …, 2015
472015
Practical Deep Learning Architecture Optimization
M Wistuba
2018 IEEE 5th International Conference on Data Science and Advanced …, 2018
46*2018
Automatic Frankensteining: Creating complex ensembles autonomously
M Wistuba, N Schilling, L Schmidt-Thieme
Proceedings of the 2017 SIAM International Conference on Data Mining, 741-749, 2017
442017
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