Matthias Hein

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Coauteurs

Francesco CroceEPFLAdresse e-mail validée de cs.uni-saarland.de
Bernt SchieleProfessor, Max Planck Institute for Informatics, Saarland University, Saarland Informatics CampusAdresse e-mail validée de mpi-inf.mpg.de
Maksym AndriushchenkoPostdoctoral Researcher at EPFLAdresse e-mail validée de epfl.ch
Ulrike von LuxburgUniversity of TübingenAdresse e-mail validée de informatik.uni-tuebingen.de
Quynh NguyenSenior Research ScientistAdresse e-mail validée de quynhnguyen.org
Francesco TudiscoThe University of EdinburghAdresse e-mail validée de ed.ac.uk
Julian BitterwolfPhD Student, University of TübingenAdresse e-mail validée de uni-tuebingen.de
David StutzResearch Scientist, DeepMindAdresse e-mail validée de deepmind.com
Nicolas FlammarionEPFLAdresse e-mail validée de epfl.ch
Anna KhorevaHead of Applied Science at ZalandoAdresse e-mail validée de zalando.de
Antoine GautierQuantPiAdresse e-mail validée de quantpi.com
Maximilian AugustinPhD Student, University of TuebingenAdresse e-mail validée de uni-tuebingen.de
Max LapinResearcher, Stability AIAdresse e-mail validée de stability.ai
Thomas BühlerAviraAdresse e-mail validée de avira.com
Olivier BousquetGoogleAdresse e-mail validée de m4x.org
Rodrigo BenensonResearch scientist at GoogleAdresse e-mail validée de google.com
Philipp HennigUniversity of TübingenAdresse e-mail validée de uni-tuebingen.de
Simon SetzerSaarland UniversityAdresse e-mail validée de mia.uni-saarland.de
Syama Sundar RangapuramAmazonAdresse e-mail validée de amazon.de
Agustinus KristiadiPostdoc, Vector InstituteAdresse e-mail validée de vectorinstitute.ai

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Matthias Hein

Professor of Computer Science, University of Tübingen

Adresse e-mail validée de uni-tuebingen.de - Page d'accueil

Machine Learning Optimization Statistics


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
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	2083	2020
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	1151	2020
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	975	2017
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	881	2016
Robustbench: a standardized adversarial robustness benchmark F Croce, M Andriushchenko, V Sehwag, E Debenedetti, N Flammarion, ... arXiv preprint arXiv:2010.09670, 2020	790	2020
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	673	2019
Formal guarantees on the robustness of a classifier against adversarial manipulation M Hein, M Andriushchenko NIPS 2017, 2017	644	2017
Minimally distorted adversarial examples with a fast adaptive boundary attack F Croce, M Hein International Conference on Machine Learning, 2196-2205, 2020	567	2020
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	413	2005
Spectral clustering based on the graph p-Laplacian T Bühler, M Hein Proceedings of the 26th annual international conference on machine learning …, 2009	391	2009
Variants of rmsprop and adagrad with logarithmic regret bounds MC Mukkamala, M Hein International conference on machine learning, 2545-2553, 2017	368	2017
Graph laplacians and their convergence on random neighborhood graphs. M Hein, JY Audibert, U Luxburg Journal of Machine Learning Research 8 (6), 2007	357	2007
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	347	2020
Disentangling adversarial robustness and generalization D Stutz, M Hein, B Schiele Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019	335	2019
The loss surface of deep and wide neural networks Q Nguyen, M Hein International conference on machine learning, 2603-2612, 2017	326	2017
Provably robust boosted decision stumps and trees against adversarial attacks M Andriushchenko, M Hein Advances in neural information processing systems 32, 2019	309	2019
Intrinsic dimensionality estimation of submanifolds in R^d M Hein, JY Audibert Proceedings of the 22nd international conference on Machine learning, 289-296, 2005	299	2005
Manifold denoising M Hein, M Maier Advances in neural information processing systems 19, 2006	257	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	255	2010
Sparse and imperceivable adversarial attacks F Croce, M Hein Proceedings of the IEEE/CVF international conference on computer vision …, 2019	251	2019

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