Matthias Hein
Matthias Hein
Professor of Computer Science, University of Tübingen
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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
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
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
Formal guarantees on the robustness of a classifier against adversarial manipulation
M Hein, M Andriushchenko
arXiv preprint arXiv:1705.08475, 2017
Spectral clustering based on the graph p-Laplacian
T Bühler, M Hein
Proceedings of the 26th Annual International Conference on Machine Learning …, 2009
Graph laplacians and their convergence on random neighborhood graphs.
M Hein, JY Audibert, U Luxburg
Journal of Machine Learning Research 8 (6), 2007
Manifold denoising
M Hein, M Maier
NIPS 19, 561-568, 2006
Intrinsic dimensionality estimation of submanifolds in Rd
M Hein, JY Audibert
Proceedings of the 22nd international conference on Machine learning, 289-296, 2005
The loss surface of deep and wide neural networks
Q Nguyen, M Hein
International conference on machine learning, 2603-2612, 2017
Hilbertian metrics and positive definite kernels on probability measures
M Hein, O Bousquet
International Workshop on Artificial Intelligence and Statistics, 136-143, 2005
Influence of graph construction on graph-based clustering measures.
M Maier, U Von Luxburg, M Hein
NIPS 1025, 1032, 2008
An inverse power method for nonlinear eigenproblems with applications in 1-spectral clustering and sparse PCA
M Hein, T Bühler
arXiv preprint arXiv:1012.0774, 2010
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
Variants of rmsprop and adagrad with logarithmic regret bounds
MC Mukkamala, M Hein
International Conference on Machine Learning, 2545-2553, 2017
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
Non-negative least squares for high-dimensional linear models: Consistency and sparse recovery without regularization
M Slawski, M Hein
Electronic Journal of Statistics 7, 3004-3056, 2013
Measure based regularization
O Bousquet, O Chapelle, M Hein
Advances in Neural Information Processing Systems, 1221-1228, 2004
Optimal construction of k-nearest-neighbor graphs for identifying noisy clusters
M Maier, M Hein, U Von Luxburg
Theoretical Computer Science 410 (19), 1749-1764, 2009
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
Learning using privileged information: SVM+ and weighted SVM
M Lapin, M Hein, B Schiele
Neural Networks 53, 95-108, 2014
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Artiklar 1–20