Matthias Hein - Google Scholar

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	All	Since 2015
Citations	5787	4186
h-index	42	36
i10-index	80	72

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Bernt SchieleProfessor, Max Planck Institute for Informatics, Saarland Informatics Campus, Saarland UniversityVerified email at mpi-inf.mpg.de
Ulrike von LuxburgUniversity of TübingenVerified email at informatik.uni-tuebingen.de
Quynh NguyenMax Planck Institute for Mathematics in the SciencesVerified email at mis.mpg.de
Anna KhorevaResearch Scientist at Bosch Center for Artificial IntelligenceVerified email at bosch.com
Francesco TudiscoGran Sasso Science InstituteVerified email at gssi.it
Thomas BühlerAviraVerified email at avira.com
Olivier BousquetGoogleVerified email at m4x.org
Maksim LapinComputer Vision Scientist, Amazon BerlinVerified email at amazon.com
Maksym AndriushchenkoPhD student, EPFLVerified email at epfl.ch
Florian SteinkeProfessor, Energy Information Networks & Systems, TU DarmstadtVerified email at eins.tu-darmstadt.de
Francesco CroceUniversity of TübingenVerified email at cs.uni-saarland.de
Syama Sundar RangapuramAmazonVerified email at amazon.de
Simon SetzerSaarland UniversityVerified email at mia.uni-saarland.de
Rodrigo BenensonResearch scientist at GoogleVerified email at google.com
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems; Professor at ETH Zürich, and DistinguishedVerified email at tuebingen.mpg.de
Pedro MercadoSaarland University - University of TübingenVerified email at cs.uni-saarland.de
Mahesh Chandra MukkamalaMathematical Optimization Group, Saarland UniversityVerified email at math.uni-sb.de
Zeynep AkataProfessor of Computer Science, University of TübingenVerified email at uni-tuebingen.de
Pavlo LutsikGerman Cancer Research CenterVerified email at dkfz.de
Kim, Kwang InSchool of Electrical and Computer Engineering, UNISTVerified email at unist.ac.kr

Matthias Hein

Professor of Computer Science, University of Tübingen

Verified email at uni-tuebingen.de - Homepage

Machine Learning Optimization Statistics


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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	356	2016
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	302	2005
Spectral clustering based on the graph p-Laplacian T Bühler, M Hein Proceedings of the 26th Annual International Conference on Machine Learning …, 2009	252	2009
Graph Laplacians and their convergence on random neighborhood graphs M Hein, JY Audibert, U Luxburg Journal of Machine Learning Research 8 (Jun), 1325-1368, 2007	247	2007
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	224	2017
Formal guarantees on the robustness of a classifier against adversarial manipulation M Hein, M Andriushchenko Advances in Neural Information Processing Systems, 2266-2276, 2017	202	2017
Intrinsic dimensionality estimation of submanifolds in R^d M Hein, JY Audibert Proceedings of the 22nd international conference on Machine learning, 289-296, 2005	202	2005
Manifold denoising M Hein, M Maier Advances in neural information processing systems, 561-568, 2007	194	2007
Hilbertian metrics and positive definite kernels on probability measures M Hein, O Bousquet Max Planck Institute for Biological Cybernetics, 2004	193	2004
Influence of graph construction on graph-based clustering measures M Maier, UV Luxburg, M Hein Advances in neural information processing systems, 1025-1032, 2009	171	2009
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, 847-855, 2010	169	2010
The loss surface of deep and wide neural networks Q Nguyen, M Hein arXiv preprint arXiv:1704.08045, 2017	159	2017
Measure based regularization O Bousquet, O Chapelle, M Hein Advances in Neural Information Processing Systems, 1221-1228, 2004	135	2004
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	134	2013
Getting lost in space: Large sample analysis of the resistance distance UV Luxburg, A Radl, M Hein Advances in Neural Information Processing Systems, 2622-2630, 2010	128	2010
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	118	2009
Semi-supervised regression using Hessian energy with an application to semi-supervised dimensionality reduction KI Kim, F Steinke, M Hein Advances in Neural Information Processing Systems, 979-987, 2009	100	2009
Variants of rmsprop and adagrad with logarithmic regret bounds MC Mukkamala, M Hein arXiv preprint arXiv:1706.05507, 2017	96	2017
Learning using privileged information: SVM+ and weighted SVM M Lapin, M Hein, B Schiele Neural Networks 53, 95-108, 2014	95	2014
Hitting and commute times in large random neighborhood graphs U Von Luxburg, A Radl, M Hein The Journal of Machine Learning Research 15 (1), 1751-1798, 2014	94	2014

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