| 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 | 978 | 2020 |
| 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 | 738 | 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 | 734 | 2017 |
| 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 | 569 | 2020 |
| Formal guarantees on the robustness of a classifier against adversarial manipulation M Hein, M Andriushchenko NIPS 2017, 2017 | 496 | 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 | 413 | 2019 |
| From Graphs to Manifolds-Weak and Strong Pointwise Consistency of Graph Laplacians. M Hein, JY Audibert, U Von Luxburg COLT 3559, 470-485, 2005 | 376 | 2005 |
| Spectral clustering based on the graph p-Laplacian T Bühler, M Hein Proceedings of the 26th annual international conference on machine learning …, 2009 | 350 | 2009 |
| Graph laplacians and their convergence on random neighborhood graphs. M Hein, JY Audibert, U Luxburg Journal of Machine Learning Research 8 (6), 2007 | 315 | 2007 |
| Robustbench: a standardized adversarial robustness benchmark F Croce, M Andriushchenko, V Sehwag, E Debenedetti, N Flammarion, ... arXiv preprint arXiv:2010.09670, 2020 | 307 | 2020 |
| Minimally distorted adversarial examples with a fast adaptive boundary attack F Croce, M Hein International Conference on Machine Learning, 2196-2205, 2020 | 298 | 2020 |
| Variants of rmsprop and adagrad with logarithmic regret bounds MC Mukkamala, M Hein International conference on machine learning, 2545-2553, 2017 | 285 | 2017 |
| The loss surface of deep and wide neural networks Q Nguyen, M Hein International conference on machine learning, 2603-2612, 2017 | 273 | 2017 |
| Intrinsic dimensionality estimation of submanifolds in Rd M Hein, JY Audibert Proceedings of the 22nd international conference on Machine learning, 289-296, 2005 | 271 | 2005 |
| Manifold denoising M Hein, M Maier Advances in neural information processing systems 19, 2006 | 246 | 2006 |
| 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 | 230 | 2010 |
| Hilbertian metrics and positive definite kernels on probability measures M Hein, O Bousquet International Workshop on Artificial Intelligence and Statistics, 136-143, 2005 | 222 | 2005 |
| Disentangling adversarial robustness and generalization D Stutz, M Hein, B Schiele Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 221 | 2019 |
| Influence of graph construction on graph-based clustering measures M Maier, U Luxburg, M Hein Advances in neural information processing systems 21, 2008 | 212 | 2008 |
| Non-negative least squares for high-dimensional linear models: Consistency and sparse recovery without regularization M Slawski, M Hein | 203 | 2013 |
Bernt SchieleProfessor, Max Planck Institute for Informatics, Saarland Informatics Campus, Saarland University確認したメール アドレス: mpi-inf.mpg.de
