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Matthias Hein
Matthias Hein
Professor of Computer Science, University of Tübingen
Verified email at uni-tuebingen.de - Homepage
Title
Cited by
Cited by
Year
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
9782020
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
7382016
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
7342017
Square attack: a query-efficient black-box adversarial attack via random search
M Andriushchenko, F Croce, N Flammarion, M Hein
Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020
5692020
Formal guarantees on the robustness of a classifier against adversarial manipulation
M Hein, M Andriushchenko
NIPS 2017, 2017
4962017
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
4132019
From Graphs to Manifolds-Weak and Strong Pointwise Consistency of Graph Laplacians.
M Hein, JY Audibert, U Von Luxburg
COLT 3559, 470-485, 2005
3762005
Spectral clustering based on the graph p-Laplacian
T Bühler, M Hein
Proceedings of the 26th annual international conference on machine learning …, 2009
3502009
Graph laplacians and their convergence on random neighborhood graphs.
M Hein, JY Audibert, U Luxburg
Journal of Machine Learning Research 8 (6), 2007
3152007
Robustbench: a standardized adversarial robustness benchmark
F Croce, M Andriushchenko, V Sehwag, E Debenedetti, N Flammarion, ...
arXiv preprint arXiv:2010.09670, 2020
3072020
Minimally distorted adversarial examples with a fast adaptive boundary attack
F Croce, M Hein
International Conference on Machine Learning, 2196-2205, 2020
2982020
Variants of rmsprop and adagrad with logarithmic regret bounds
MC Mukkamala, M Hein
International conference on machine learning, 2545-2553, 2017
2852017
The loss surface of deep and wide neural networks
Q Nguyen, M Hein
International conference on machine learning, 2603-2612, 2017
2732017
Intrinsic dimensionality estimation of submanifolds in Rd
M Hein, JY Audibert
Proceedings of the 22nd international conference on Machine learning, 289-296, 2005
2712005
Manifold denoising
M Hein, M Maier
Advances in neural information processing systems 19, 2006
2462006
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
2302010
Hilbertian metrics and positive definite kernels on probability measures
M Hein, O Bousquet
International Workshop on Artificial Intelligence and Statistics, 136-143, 2005
2222005
Disentangling adversarial robustness and generalization
D Stutz, M Hein, B Schiele
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019
2212019
Influence of graph construction on graph-based clustering measures
M Maier, U Luxburg, M Hein
Advances in neural information processing systems 21, 2008
2122008
Non-negative least squares for high-dimensional linear models: Consistency and sparse recovery without regularization
M Slawski, M Hein
2032013
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