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ORCIDMatthias Hein 0001
Person information
- affiliation: Max Planck Institute for Intelligent Systems, Tübingen, Germany
- affiliation: Faculty of Mathematics and Computer Science, Saarland University
- affiliation: Max Planck Institute for Biological Cybernetics, Tübingen, Germany
Other persons with the same name
- Matthias Hein
- Matthias Hein 0002
![[0000-0002-8751-7760]](https://dblp.org/img/orcid-mark.12x12.png "0000-0002-8751-7760")
(aka: Matthias A. Hein) — Technische Universität Ilmenau, Faculty of Computer Science and Automation, Germany - Matthias Hein 0003 — University of Wuppertal, Department of Physics, Germany
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2020 – today
- 2022
[c80]Francesco Croce, Maksym Andriushchenko, Naman D. Singh, Nicolas Flammarion, Matthias Hein:
Sparse-RS: A Versatile Framework for Query-Efficient Sparse Black-Box Adversarial Attacks. AAAI 2022: 6437-6445- [c79]Agustinus Kristiadi, Matthias Hein, Philipp Hennig:
Being a Bit Frequentist Improves Bayesian Neural Networks. AISTATS 2022: 529-545 - [c78]Julian Bitterwolf, Alexander Meinke, Maximilian Augustin, Matthias Hein:
Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core Quantities. ICML 2022: 2041-2074 - [c77]Francesco Croce, Sven Gowal, Thomas Brunner, Evan Shelhamer, Matthias Hein, A. Taylan Cemgil:
Evaluating the Adversarial Robustness of Adaptive Test-time Defenses. ICML 2022: 4421-4435 - [c76]Francesco Croce, Matthias Hein:
Adversarial Robustness against Multiple and Single lp-Threat Models via Quick Fine-Tuning of Robust Classifiers. ICML 2022: 4436-4454 - [c75]Václav Vorácek, Matthias Hein:
Provably Adversarially Robust Nearest Prototype Classifiers. ICML 2022: 22361-22383 - [c74]Daniel Heller, Patrick Ferber, Julian Bitterwolf, Matthias Hein, Jörg Hoffmann:
Neural Network Heuristic Functions: Taking Confidence into Account. SOCS 2022: 223-228 - [i76]Francesco Croce, Sven Gowal, Thomas Brunner, Evan Shelhamer, Matthias Hein, A. Taylan Cemgil:
Evaluating the Adversarial Robustness of Adaptive Test-time Defenses. CoRR abs/2202.13711 (2022) - [i75]Valentyn Boreiko, Maximilian Augustin, Francesco Croce, Philipp Berens, Matthias Hein:
Sparse Visual Counterfactual Explanations in Image Space. CoRR abs/2205.07972 (2022) - [i74]Julian Bitterwolf, Alexander Meinke, Maximilian Augustin, Matthias Hein:
Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core Quantities. CoRR abs/2206.09880 (2022) - [i73]Václav Vorácek, Matthias Hein:
Provably Adversarially Robust Nearest Prototype Classifiers. CoRR abs/2207.07208 (2022) - [i72]Václav Vorácek, Matthias Hein:
Sound Randomized Smoothing in Floating-Point Arithmetics. CoRR abs/2207.07209 (2022) - 2021
- [j19]Antoine Gautier, Matthias Hein, Francesco Tudisco:
The Global Convergence of the Nonlinear Power Method for Mixed-Subordinate Matrix Norms. J. Sci. Comput. 88(1): 21 (2021) - [c73]David Stutz, Matthias Hein, Bernt Schiele:
Relating Adversarially Robust Generalization to Flat Minima. ICCV 2021: 7787-7797 - [c72]Francesco Croce, Matthias Hein:
Mind the Box: l1-APGD for Sparse Adversarial Attacks on Image Classifiers. ICML 2021: 2201-2211 - [c71]David Stutz, Nandhini Chandramoorthy, Matthias Hein, Bernt Schiele:
Bit Error Robustness for Energy-Efficient DNN Accelerators. MLSys 2021 - [c70]Francesco Croce, Maksym Andriushchenko, Vikash Sehwag, Edoardo Debenedetti, Nicolas Flammarion, Mung Chiang, Prateek Mittal, Matthias Hein:
RobustBench: a standardized adversarial robustness benchmark. NeurIPS Datasets and Benchmarks 2021 - [c69]Agustinus Kristiadi, Matthias Hein, Philipp Hennig:
An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence. NeurIPS 2021: 18789-18800 - [c68]Maksym Yatsura, Jan Hendrik Metzen, Matthias Hein:
Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial Attacks. NeurIPS 2021: 30181-30195 - [c67]Agustinus Kristiadi, Matthias Hein, Philipp Hennig:
Learnable uncertainty under Laplace approximations. UAI 2021: 344-353 - [i71]Francesco Croce, Matthias Hein:
Mind the box: l1-APGD for sparse adversarial attacks on image classifiers. CoRR abs/2103.01208 (2021) - [i70]David Stutz, Matthias Hein, Bernt Schiele:
Relating Adversarially Robust Generalization to Flat Minima. CoRR abs/2104.04448 (2021) - [i69]David Stutz, Nandhini Chandramoorthy, Matthias Hein, Bernt Schiele:
Random and Adversarial Bit Error Robustness: Energy-Efficient and Secure DNN Accelerators. CoRR abs/2104.08323 (2021) - [i68]Francesco Croce, Matthias Hein:
Adversarial robustness against multiple lp-threat models at the price of one and how to quickly fine-tune robust models to another threat model. CoRR abs/2105.12508 (2021) - [i67]Alexander Meinke, Julian Bitterwolf, Matthias Hein:
Provably Robust Detection of Out-of-distribution Data (almost) for free. CoRR abs/2106.04260 (2021) - [i66]Agustinus Kristiadi, Matthias Hein, Philipp Hennig:
Being a Bit Frequentist Improves Bayesian Neural Networks. CoRR abs/2106.10065 (2021) - [i65]Maksym Yatsura, Jan Hendrik Metzen, Matthias Hein:
Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial Attacks. CoRR abs/2111.01714 (2021) - 2020
- [j18]Nicolás García Trillos, Moritz Gerlach, Matthias Hein, Dejan Slepcev:
Error Estimates for Spectral Convergence of the Graph Laplacian on Random Geometric Graphs Toward the Laplace-Beltrami Operator. Found. Comput. Math. 20(4): 827-887 (2020) - [j17]Francesco Croce, Jonas Rauber, Matthias Hein:
Scaling up the Randomized Gradient-Free Adversarial Attack Reveals Overestimation of Robustness Using Established Attacks. Int. J. Comput. Vis. 128(4): 1028-1046 (2020) - [c66]Maximilian Augustin, Alexander Meinke, Matthias Hein:
Adversarial Robustness on In- and Out-Distribution Improves Explainability. ECCV (26) 2020: 228-245 - [c65]Maksym Andriushchenko, Francesco Croce, Nicolas Flammarion, Matthias Hein:
Square Attack: A Query-Efficient Black-Box Adversarial Attack via Random Search. ECCV (23) 2020: 484-501 - [c64]Francesco Croce, Matthias Hein:
Provable robustness against all adversarial $l_p$-perturbations for $p\geq 1$. ICLR 2020 - [c63]Alexander Meinke, Matthias Hein:
Towards neural networks that provably know when they don't know. ICLR 2020 - [c62]Francesco Croce, Matthias Hein:
Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack. ICML 2020: 2196-2205 - [c61]Francesco Croce, Matthias Hein:
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks. ICML 2020: 2206-2216 - [c60]Agustinus Kristiadi, Matthias Hein, Philipp Hennig:
Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks. ICML 2020: 5436-5446 - [c59]David Stutz, Matthias Hein, Bernt Schiele:
Confidence-Calibrated Adversarial Training: Generalizing to Unseen Attacks. ICML 2020: 9155-9166 - [c58]Julian Bitterwolf, Alexander Meinke, Matthias Hein:
Certifiably Adversarially Robust Detection of Out-of-Distribution Data. NeurIPS 2020 - [i64]Antoine Gautier, Matthias Hein, Francesco Tudisco:
Computing the norm of nonnegative matrices and the log-Sobolev constant of Markov chains. CoRR abs/2002.02447 (2020) - [i63]Agustinus Kristiadi, Matthias Hein, Philipp Hennig:
Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks. CoRR abs/2002.10118 (2020) - [i62]Francesco Croce, Matthias Hein:
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks. CoRR abs/2003.01690 (2020) - [i61]Maximilian Augustin, Alexander Meinke, Matthias Hein:
Adversarial Robustness on In- and Out-Distribution Improves Explainability. CoRR abs/2003.09461 (2020) - [i60]Francesco Croce, Maksym Andriushchenko, Naman D. Singh, Nicolas Flammarion, Matthias Hein:
Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks. CoRR abs/2006.12834 (2020) - [i59]David Stutz, Nandhini Chandramoorthy, Matthias Hein, Bernt Schiele:
On Mitigating Random and Adversarial Bit Errors. CoRR abs/2006.13977 (2020) - [i58]Julian Bitterwolf, Alexander Meinke, Matthias Hein:
Provable Worst Case Guarantees for the Detection of Out-of-Distribution Data. CoRR abs/2007.08473 (2020) - [i57]Agustinus Kristiadi, Matthias Hein, Philipp Hennig:
Fixing Asymptotic Uncertainty of Bayesian Neural Networks with Infinite ReLU Features. CoRR abs/2010.02709 (2020) - [i56]Agustinus Kristiadi, Matthias Hein, Philipp Hennig:
Learnable Uncertainty under Laplace Approximations. CoRR abs/2010.02720 (2020) - [i55]Francesco Croce, Maksym Andriushchenko, Vikash Sehwag, Nicolas Flammarion, Mung Chiang, Prateek Mittal, Matthias Hein:
RobustBench: a standardized adversarial robustness benchmark. CoRR abs/2010.09670 (2020) - [i54]Maximilian Augustin, Matthias Hein:
Out-distribution aware Self-training in an Open World Setting. CoRR abs/2012.12372 (2020)
2010 – 2019
- 2019
- [j16]Antoine Gautier, Francesco Tudisco, Matthias Hein:
The Perron-Frobenius Theorem for Multihomogeneous Mappings. SIAM J. Matrix Anal. Appl. 40(3): 1179-1205 (2019) - [j15]Antoine Gautier, Francesco Tudisco, Matthias Hein:
A Unifying Perron-Frobenius Theorem for Nonnegative Tensors via Multihomogeneous Maps. SIAM J. Matrix Anal. Appl. 40(3): 1206-1231 (2019) - [c57]Francesco Croce, Maksym Andriushchenko, Matthias Hein:
Provable Robustness of ReLU networks via Maximization of Linear Regions. AISTATS 2019: 2057-2066 - [c56]Matthias Hein, Maksym Andriushchenko, Julian Bitterwolf:
Why ReLU Networks Yield High-Confidence Predictions Far Away From the Training Data and How to Mitigate the Problem. CVPR 2019: 41-50 - [c55]Matthias Hein, Maksym Andriushchenko, Julian Bitterwolf:
Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem. CVPR Workshops 2019: 58-74 - [c54]David Stutz, Matthias Hein, Bernt Schiele:
Disentangling Adversarial Robustness and Generalization. CVPR 2019: 6976-6987 - [c53]Francesco Croce, Matthias Hein:
Sparse and Imperceivable Adversarial Attacks. ICCV 2019: 4723-4731 - [c52]Quynh Nguyen, Mahesh Chandra Mukkamala, Matthias Hein:
On the loss landscape of a class of deep neural networks with no bad local valleys. ICLR (Poster) 2019 - [c51]Pedro Mercado, Francesco Tudisco, Matthias Hein:
Spectral Clustering of Signed Graphs via Matrix Power Means. ICML 2019: 4526-4536 - [c50]Maksym Andriushchenko, Matthias Hein:
Provably robust boosted decision stumps and trees against adversarial attacks. NeurIPS 2019: 12997-13008 - [c49]Pedro Mercado, Francesco Tudisco, Matthias Hein:
Generalized Matrix Means for Semi-Supervised Learning with Multilayer Graphs. NeurIPS 2019: 14848-14857 - [i53]Francesco Croce, Jonas Rauber, Matthias Hein:
Scaling up the randomized gradient-free adversarial attack reveals overestimation of robustness using established attacks. CoRR abs/1903.11359 (2019) - [i52]Pedro Mercado, Francesco Tudisco, Matthias Hein:
Spectral Clustering of Signed Graphs via Matrix Power Means. CoRR abs/1905.06230 (2019) - [i51]Francesco Croce, Matthias Hein:
Provable robustness against all adversarial lp-perturbations for p≥1. CoRR abs/1905.11213 (2019) - [i50]Maksym Andriushchenko, Matthias Hein:
Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks. CoRR abs/1906.03526 (2019) - [i49]Francesco Croce, Matthias Hein:
Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack. CoRR abs/1907.02044 (2019) - [i48]Francesco Croce, Matthias Hein:
Sparse and Imperceivable Adversarial Attacks. CoRR abs/1909.05040 (2019) - [i47]Alexander Meinke, Matthias Hein:
Towards neural networks that provably know when they don't know. CoRR abs/1909.12180 (2019) - [i46]David Stutz, Matthias Hein, Bernt Schiele:
Confidence-Calibrated Adversarial Training: Towards Robust Models Generalizing Beyond the Attack Used During Training. CoRR abs/1910.06259 (2019) - [i45]Pedro Mercado, Francesco Tudisco, Matthias Hein:
Generalized Matrix Means for Semi-Supervised Learning with Multilayer Graphs. CoRR abs/1910.13951 (2019) - [i44]Maksym Andriushchenko, Francesco Croce, Nicolas Flammarion, Matthias Hein:
Square Attack: a query-efficient black-box adversarial attack via random search. CoRR abs/1912.00049 (2019) - 2018
- [j14]Maksim Lapin, Matthias Hein, Bernt Schiele:
Analysis and Optimization of Loss Functions for Multiclass, Top-k, and Multilabel Classification. IEEE Trans. Pattern Anal. Mach. Intell. 40(7): 1533-1554 (2018) - [j13]Francesco Tudisco, Pedro Mercado, Matthias Hein:
Community Detection in Networks via Nonlinear Modularity Eigenvectors. SIAM J. Appl. Math. 78(5): 2393-2419 (2018) - [c48]Pedro Mercado, Antoine Gautier, Francesco Tudisco, Matthias Hein:
The Power Mean Laplacian for Multilayer Graph Clustering. AISTATS 2018: 1828-1838 - [c47]Francesco Croce, Matthias Hein:
A Randomized Gradient-Free Attack on ReLU Networks. GCPR 2018: 215-227 - [c46]Quynh Nguyen, Matthias Hein:
The loss surface and expressivity of deep convolutional neural networks. ICLR (Workshop) 2018 - [c45]Quynh Nguyen, Matthias Hein:
Optimization Landscape and Expressivity of Deep CNNs. ICML 2018: 3727-3736 - [c44]Quynh Nguyen, Mahesh Chandra Mukkamala, Matthias Hein:
Neural Networks Should Be Wide Enough to Learn Disconnected Decision Regions. ICML 2018: 3737-3746 - [i43]Antoine Gautier, Francesco Tudisco, Matthias Hein:
A unifying Perron-Frobenius theorem for nonnegative tensors via multi-homogeneous maps. CoRR abs/1801.04215 (2018) - [i42]Quynh Nguyen, Mahesh Chandra Mukkamala, Matthias Hein:
Neural Networks Should Be Wide Enough to Learn Disconnected Decision Regions. CoRR abs/1803.00094 (2018) - [i41]Pedro Mercado, Antoine Gautier, Francesco Tudisco, Matthias Hein:
The Power Mean Laplacian for Multilayer Graph Clustering. CoRR abs/1803.00491 (2018) - [i40]Quynh Nguyen, Mahesh Chandra Mukkamala, Matthias Hein:
On the loss landscape of a class of deep neural networks with no bad local valleys. CoRR abs/1809.10749 (2018) - [i39]Francesco Croce, Maksym Andriushchenko, Matthias Hein:
Provable Robustness of ReLU networks via Maximization of Linear Regions. CoRR abs/1810.07481 (2018) - [i38]Marius Mosbach, Maksym Andriushchenko, Thomas Alexander Trost, Matthias Hein, Dietrich Klakow:
Logit Pairing Methods Can Fool Gradient-Based Attacks. CoRR abs/1810.12042 (2018) - [i37]Francesco Croce, Matthias Hein:
A randomized gradient-free attack on ReLU networks. CoRR abs/1811.11493 (2018) - [i36]David Stutz, Matthias Hein, Bernt Schiele:
Disentangling Adversarial Robustness and Generalization. CoRR abs/1812.00740 (2018) - [i35]Matthias Hein, Maksym Andriushchenko, Julian Bitterwolf:
Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem. CoRR abs/1812.05720 (2018) - 2017
- [j12]Quynh Nguyen, Francesco Tudisco, Antoine Gautier, Matthias Hein:
An Efficient Multilinear Optimization Framework for Hypergraph Matching. IEEE Trans. Pattern Anal. Mach. Intell. 39(6): 1054-1075 (2017) - [c43]Anna Khoreva, Rodrigo Benenson, Jan Hendrik Hosang, Matthias Hein, Bernt Schiele:
Simple Does It: Weakly Supervised Instance and Semantic Segmentation. CVPR 2017: 1665-1674 - [c42]Mahesh Chandra Mukkamala, Matthias Hein:
Variants of RMSProp and Adagrad with Logarithmic Regret Bounds. ICML 2017: 2545-2553 - [c41]Quynh Nguyen, Matthias Hein:
The Loss Surface of Deep and Wide Neural Networks. ICML 2017: 2603-2612 - [c40]Matthias Hein, Maksym Andriushchenko:
Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation. NIPS 2017: 2266-2276 - [i34]Pedro Mercado, Francesco Tudisco, Matthias Hein:
Clustering Signed Networks with the Geometric Mean of Laplacians. CoRR abs/1701.00757 (2017) - [i33]Quynh Nguyen, Matthias Hein:
The loss surface of deep and wide neural networks. CoRR abs/1704.08045 (2017) - [i32]Matthias Hein, Maksym Andriushchenko:
Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation. CoRR abs/1705.08475 (2017) - [i31]Mahesh Chandra Mukkamala, Matthias Hein:
Variants of RMSProp and Adagrad with Logarithmic Regret Bounds. CoRR abs/1706.05507 (2017) - [i30]Francesco Tudisco, Pedro Mercado, Matthias Hein:
Community detection in networks via nonlinear modularity eigenvectors. CoRR abs/1708.05569 (2017) - [i29]Quynh Nguyen, Matthias Hein:
The loss surface and expressivity of deep convolutional neural networks. CoRR abs/1710.10928 (2017) - 2016
- [c39]Yongqin Xian, Zeynep Akata, Gaurav Sharma, Quynh Nguyen, Matthias Hein, Bernt Schiele:
Latent Embeddings for Zero-Shot Classification. CVPR 2016: 69-77 - [c38]Anna Khoreva, Rodrigo Benenson, Mohamed Omran, Matthias Hein, Bernt Schiele:
Weakly Supervised Object Boundaries. CVPR 2016: 183-192 - [c37]Maksim Lapin, Matthias Hein, Bernt Schiele:
Loss Functions for Top-k Error: Analysis and Insights. CVPR 2016: 1468-1477 - [c36]Anna Khoreva, Rodrigo Benenson, Fabio Galasso, Matthias Hein, Bernt Schiele:
Improved Image Boundaries for Better Video Segmentation. ECCV Workshops (3) 2016: 773-788 - [c35]Antoine Gautier, Quynh Nguyen, Matthias Hein:
Globally Optimal Training of Generalized Polynomial Neural Networks with Nonlinear Spectral Methods. NIPS 2016: 1687-1695 - [c34]Pedro Mercado, Francesco Tudisco, Matthias Hein:
Clustering Signed Networks with the Geometric Mean of Laplacians. NIPS 2016: 4421-4429 - [i28]Francesco Tudisco, Matthias Hein:
Nodal domain theorem for the graph p-Laplacian. CoRR abs/1602.05567 (2016) - [i27]Anna Khoreva, Rodrigo Benenson, Jan Hendrik Hosang, Matthias Hein, Bernt Schiele:
Weakly Supervised Semantic Labelling and Instance Segmentation. CoRR abs/1603.07485 (2016) - [i26]Yongqin Xian, Zeynep Akata, Gaurav Sharma, Quynh Nguyen, Matthias Hein, Bernt Schiele:
Latent Embeddings for Zero-shot Classification. CoRR abs/1603.08895 (2016) - [i25]Anna Khoreva, Rodrigo Benenson, Fabio Galasso, Matthias Hein, Bernt Schiele:
Improved Image Boundaries for Better Video Segmentation. CoRR abs/1605.03718 (2016) - [i24]Antoine Gautier, Quynh Nguyen, Matthias Hein:
Globally Optimal Training of Generalized Polynomial Neural Networks with Nonlinear Spectral Methods. CoRR abs/1610.09300 (2016) - [i23]Maksim Lapin, Matthias Hein, Bernt Schiele:
Analysis and Optimization of Loss Functions for Multiclass, Top-k, and Multilabel Classification. CoRR abs/1612.03663 (2016) - 2015
- [j11]Sahely Bhadra, Matthias Hein:
Correction of noisy labels via mutual consistency check. Neurocomputing 160: 34-52 (2015) - [c33]Anna Khoreva, Fabio Galasso, Matthias Hein, Bernt Schiele:
Classifier based graph construction for video segmentation. CVPR 2015: 951-960 - [c32]Quynh Nguyen Ngoc, Antoine Gautier, Matthias Hein:
A flexible tensor block coordinate ascent scheme for hypergraph matching. CVPR 2015: 5270-5278 - [c31]Maksim Lapin, Matthias Hein, Bernt Schiele:
Top-k Multiclass SVM. NIPS 2015: 325-333 - [c30]Pratik Jawanpuria, Maksim Lapin, Matthias Hein, Bernt Schiele:
Efficient Output Kernel Learning for Multiple Tasks. NIPS 2015: 1189-1197 - [c29]Martin Slawski, Ping Li, Matthias Hein:
Regularization-Free Estimation in Trace Regression with Symmetric Positive Semidefinite Matrices. NIPS 2015: 2782-2790 - [i22]Martin Slawski, Ping Li, Matthias Hein:
Regularization-free estimation in trace regression with symmetric positive semidefinite matrices. CoRR abs/1504.06305 (2015) - [i21]Quynh Nguyen Ngoc, Antoine Gautier, Matthias Hein:
A Flexible Tensor Block Coordinate Ascent Scheme for Hypergraph Matching. CoRR abs/1504.07907 (2015) - [i20]Syama Sundar Rangapuram, Pramod Kaushik Mudrakarta, Matthias Hein:
Tight Continuous Relaxation of the Balanced k-Cut Problem. CoRR abs/1505.06478 (2015) - [i19]Syama Sundar Rangapuram, Matthias Hein:
Constrained 1-Spectral Clustering. CoRR abs/1505.06485 (2015) - [i18]Syama Sundar Rangapuram, Thomas Bühler, Matthias Hein:
Towards Realistic Team Formation in Social Networks based on Densest Subgraphs. CoRR abs/1505.06661 (2015) - [i17]Anastasia Podosinnikova, Simon Setzer, Matthias Hein:
Robust PCA: Optimization of the Robust Reconstruction Error over the Stiefel Manifold. CoRR abs/1506.00323 (2015) - [i16]Quynh Nguyen, Francesco Tudisco, Antoine Gautier, Matthias Hein:
An Efficient Multilinear Optimization Framework for Hypergraph Matching. CoRR abs/1511.02667 (2015) - [i15]Pratik Jawanpuria, Maksim Lapin, Matthias Hein, Bernt Schiele:
Efficient Output Kernel Learning for Multiple Tasks. CoRR abs/1511.05706 (2015) - [i14]Maksim Lapin, Matthias Hein, Bernt Schiele:
Top-k Multiclass SVM. CoRR abs/1511.06683 (2015) - [i13]Anna Khoreva, Rodrigo Benenson, Mohamed Omran, Matthias Hein, Bernt Schiele:
Weakly Supervised Object Boundaries. CoRR abs/1511.07803 (2015) - [i12]Maksim Lapin, Matthias Hein, Bernt Schiele:
Loss Functions for Top-k Error: Analysis and Insights. CoRR abs/1512.00486 (2015) - [i11]Matthias Hein, Gábor Lugosi, Lorenzo Rosasco:
Mathematical and Computational Foundations of Learning Theory (Dagstuhl Seminar 15361). Dagstuhl Reports 5(8): 54-0 (2015) - 2014
- [j10]Ulrike von Luxburg, Agnes Radl, Matthias Hein:
Hitting and commute times in large random neighborhood graphs. J. Mach. Learn. Res. 15(1): 1751-1798 (2014) - [j9]Maksim Lapin, Matthias Hein, Bernt Schiele:
Learning using privileged information: SV M+ and weighted SVM. Neural Networks 53: 95-108 (2014) - [c28]Maksim Lapin, Bernt Schiele, Matthias Hein:
Scalable Multitask Representation Learning for Scene Classification. CVPR 2014: 1434-1441 - [c27]Anastasia Podosinnikova, Simon Setzer, Matthias Hein:
Robust PCA: Optimization of the Robust Reconstruction Error Over the Stiefel Manifold. GCPR 2014: 121-131 - [c26]