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Matthias Hein
Matthias Hein
Professor of Computer Science, University of Tübingen
Bestätigte E-Mail-Adresse bei uni-tuebingen.de - Startseite
Titel
Zitiert von
Zitiert von
Jahr
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
F Croce, M Hein
International conference on machine learning, 2206-2216, 2020
12912020
Simple does it: Weakly supervised instance and semantic segmentation
A Khoreva, R Benenson, J Hosang, M Hein, B Schiele
Proceedings of the IEEE conference on computer vision and pattern …, 2017
8102017
Latent embeddings for zero-shot classification
Y Xian, Z Akata, G Sharma, Q Nguyen, M Hein, B Schiele
Proceedings of the IEEE conference on computer vision and pattern …, 2016
7882016
Square attack: a query-efficient black-box adversarial attack via random search
M Andriushchenko, F Croce, N Flammarion, M Hein
European conference on computer vision, 484-501, 2020
7322020
Formal guarantees on the robustness of a classifier against adversarial manipulation
M Hein, M Andriushchenko
NIPS 2017, 2017
5342017
Why relu networks yield high-confidence predictions far away from the training data and how to mitigate the problem
M Hein, M Andriushchenko, J Bitterwolf
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019
4932019
Robustbench: a standardized adversarial robustness benchmark
F Croce, M Andriushchenko, V Sehwag, E Debenedetti, N Flammarion, ...
arXiv preprint arXiv:2010.09670, 2020
4322020
From graphs to manifolds–weak and strong pointwise consistency of graph Laplacians
M Hein, JY Audibert, U Von Luxburg
International Conference on Computational Learning Theory, 470-485, 2005
3952005
Minimally distorted adversarial examples with a fast adaptive boundary attack
F Croce, M Hein
International Conference on Machine Learning, 2196-2205, 2020
3752020
Spectral clustering based on the graph p-Laplacian
T Bühler, M Hein
Proceedings of the 26th annual international conference on machine learning …, 2009
3642009
Graph laplacians and their convergence on random neighborhood graphs.
M Hein, JY Audibert, U Luxburg
Journal of Machine Learning Research 8 (6), 2007
3312007
Variants of rmsprop and adagrad with logarithmic regret bounds
MC Mukkamala, M Hein
International conference on machine learning, 2545-2553, 2017
3172017
The loss surface of deep and wide neural networks
Q Nguyen, M Hein
International conference on machine learning, 2603-2612, 2017
2842017
Intrinsic dimensionality estimation of submanifolds in Rd
M Hein, JY Audibert
Proceedings of the 22nd international conference on Machine learning, 289-296, 2005
2842005
Disentangling adversarial robustness and generalization
D Stutz, M Hein, B Schiele
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019
2592019
Manifold denoising
M Hein, M Maier
Advances in neural information processing systems 19, 2006
2502006
An inverse power method for nonlinear eigenproblems with applications in 1-spectral clustering and sparse PCA
M Hein, T Bühler
Advances in neural information processing systems 23, 2010
2372010
Hilbertian metrics and positive definite kernels on probability measures
M Hein, O Bousquet
International Workshop on Artificial Intelligence and Statistics, 136-143, 2005
2292005
Being bayesian, even just a bit, fixes overconfidence in relu networks
A Kristiadi, M Hein, P Hennig
International conference on machine learning, 5436-5446, 2020
2222020
Influence of graph construction on graph-based clustering measures
M Maier, U Luxburg, M Hein
Advances in neural information processing systems 21, 2008
2202008
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