Matthias Hein

发表

Intrinsic Dimensionality Estimation of Submanifolds in Euclidean space

Matthias Hein, L. Wrobel, 2005, ICML 2005.

Maximal Margin Classification for Metric Spaces

O. Bousquet, Matthias Hein, 2003, COLT 2003.

Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation

Matthias Hein, Maksym Andriushchenko, Matthias Hein, 2017, NIPS.

Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks

Naman D. Singh, Nicolas Flammarion, Matthias Hein, 2020, ArXiv.

Kernels, Associated Structures and Generalizations

O. Bousquet, Matthias Hein, 2004 .

Non-negative least squares for high-dimensional linear models: consistency and sparse recovery without regularization

Matthias Hein, M. Slawski, 2012, 1205.0953.

The Loss Surface of Deep and Wide Neural Networks

Matthias Hein, Quynh Nguyen, Matthias Hein, 2017, ICML.

Variants of RMSProp and Adagrad with Logarithmic Regret Bounds

Matthias Hein, Mahesh Chandra Mukkamala, Matthias Hein, 2017, ICML.

Hilbertian Metrics on Probability Measures and Their Application in SVM?s

Matthias Hein, Thomas Navin Lal, Olivier Bousquet, 2004, DAGM-Symposium.

Hitting times, commute distances and the spectral gap for large random geometric graphs pdf

Ulrike von Luxburg, Matthias Hein, Agnes Radl, 2010, ArXiv.

Constrained 1-Spectral Clustering pdf

Matthias Hein, Syama Sundar Rangapuram, Matthias Hein, 2012, AISTATS.

Hilbertian Metrics and Positive Definite Kernels on Probability Measures

Matthias Hein, Olivier Bousquet, O. Bousquet, 2005, AISTATS.

Uniform Convergence of Adaptive Graph-Based Regularization

Matthias Hein, Matthias Hein, 2006, COLT.

Maximal Margin Classification for Metric Spaces

Matthias Hein, Olivier Bousquet, O. Bousquet, 2003, COLT.

Spectral clustering based on the graph p-Laplacian

Matthias Hein, Thomas Bühler, Matthias Hein, 2009, ICML '09.

Geometrical aspects of statistical learning theory

Matthias Hein, Matthias Hein, 2005 .

Intrinsic Dimensionality Estimation of Submanifolds in R

Matthias Hein, 2005 .

Getting lost in space: large sample analysis of the commute distance

U. V. Luxburg, Matthias Hein, Agnes Radl, 2010, NIPS 2010.

Manifold‐valued Thin‐Plate Splines with Applications in Computer Graphics

Bernhard Schölkopf, Florian Steinke, Matthias Hein, 2008, Comput. Graph. Forum.

Energy Functionals for Manifold-valued Mappings and Their Properties

B. Schölkopf, Matthias Hein, F. Steinke, 2008 .

Measure Based Regularization

Matthias Hein, Olivier Chapelle, Olivier Bousquet, 2003, NIPS.

Learning Must-Link Constraints for Video Segmentation Based on Spectral Clustering

Bernt Schiele, Matthias Hein, Anna Khoreva, 2014, GCPR.

Optimal construction of k-nearest-neighbor graphs for identifying noisy clusters

Ulrike von Luxburg, Matthias Hein, Markus Maier, 2009, Theoretical Computer Science.

Top-k Multiclass SVM

Bernt Schiele, Matthias Hein, Maksim Lapin, 2015, NIPS.

An integer linear programming approach for finding deregulated subgraphs in regulatory networks

Christina Backes, Eckart Meese, Andreas Keller, 2011, Nucleic acids research.

The Perron-Frobenius theorem for multi-homogeneous maps

Matthias Hein, A. Gautier, Francesco Tudisco, 2017, 1702.03230.

Tensor norm and maximal singular vectors of non-negative tensors - a Perron-Frobenius theorem, a Collatz-Wielandt characterization and a generalized power method

Matthias Hein, Antoine Gautier, Matthias Hein, 2015, 1503.01273.

Adversarial Robustness on In- and Out-Distribution Improves Explainability

Matthias Hein, Alexander Meinke, Maximilian Augustin, 2020, ECCV.

Overview of Talks Convex Risks , Calibrated Surrogates , Consistency , and Their Relationship with Nonparametric Estimation

G. Lugosi, L. Rosasco, Matthias Hein, 2016 .

Matrix factorization with binary components

Matthias Hein, Pavlo Lutsik, Martin Slawski, 2013, NIPS.

CONFIDENCE-CALIBRATED ADVERSARIAL TRAINING

B. Schiele, Matthias Hein, David Stutz, 2019 .

Supplementary Material for Disentangling Adversarial Robustness and Generalization

B. Schiele, Matthias Hein, David Stutz, 2019 .

FONTS True Manifold FONTS Learned Manifold EMNIST Learned Manifold F-MNIST Learned Manifold CelebA

B. Schiele, Matthias Hein, David Stutz, 2022 .

Estimation of positive definite M-matrices and structure learning for attractive Gaussian Markov Random fields

Matthias Hein, M. Slawski, 2014, 1404.6640.

Nonlinear Spectral Methods for Nonconvex Optimization with Global Optimality

Matthias Hein, Quynh N. Nguyen, A. Gautier, 2016 .

COMMUNITY DETECTION IN NETWORKS VIA NONLINEAR

Matthias Hein, Francesco Tudisco, Pedro Mercado, 2018 .

RobustBench: a standardized adversarial robustness benchmark

Nicolas Flammarion, Prateek Mittal, Matthias Hein, 2020, NeurIPS Datasets and Benchmarks.

Simplex Coding and Supervised Dictionary Learning

Bernt Schiele, Matthias Hein, Maksim Lapin, 2012 .

Video Segmentation with Graph Cuts

Bernt Schiele, Matthias Hein, Anna Khoreva, 2014 .

Nodal domain theorem for the graph p-Laplacian pdf

Matthias Hein, Francesco Tudisco, Matthias Hein, 2016, ArXiv.

Relating Adversarially Robust Generalization to Flat Minima pdf

Bernt Schiele, Matthias Hein, David Stutz, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).

Sparse Dictionary Learning with Simplex Constraints and Application to Topic Modeling

Matthias Hein, Qinqing Zheng, Martin Slawski, 2012 .

Random and Adversarial Bit Error Robustness: Energy-Efficient and Secure DNN Accelerators pdf

Bernt Schiele, Matthias Hein, David Stutz, 2021, ArXiv.

Optimization Algorithms in the Reconstruction of MR Images: A Comparative Study

Matthias Hein, Mateusz Malinowski, Matthias Seeger, 2011 .

Clustering and Community Detection in Signed Networks

Matthias Hein, Bjoern Andres, Pedro Eduardo Mercado Lopez, 2015 .

A Tensor Block Coordinate Ascent Framework for Hyper Graph Matching

Joachim Weickert, Matthias Hein, Ngoc Quynh Nguyen, 2015 .

Adversarial Robustness against Multiple and Single lp-Threat Models via Quick Fine-Tuning of Robust Classifiers

Matthias Hein, Francesco Croce, 2021, ICML.

Sparse recovery for Protein Mass Spectrometry data

Matthias Hein, M. Slawski, 2010 .

Certified Defences Against Adversarial Patch Attacks on Semantic Segmentation

J. H. Metzen, Matthias Hein, K. Sakmann, 2022, ArXiv.

Evaluating the Adversarial Robustness of Adaptive Test-time Defenses

Evan Shelhamer, Matthias Hein, A. Cemgil, 2022, ICML.

Adversarial Robustness of MR Image Reconstruction under Realistic Perturbations

Christian F. Baumgartner, Matthias Hein, S. Gatidis, 2022, MLMIR@MICCAI.

Confidence-Calibrated Adversarial Training and Detection: More Robust Models Generalizing Beyond the Attack Used During Training

Bernt Schiele, David Stutz, Matthias Hein, 2019 .

Direct calculation of depth of correlation and weighting function in ?PIV from experimental particle images

Matthias Hein, B. Wieneke, R. Seemann, 2013 .

Sparse Visual Counterfactual Explanations in Image Space

Philipp Berens, Matthias Hein, Francesco Croce, 2022, GCPR.

Provably Adversarially Robust Detection of Out-of-Distribution Data (Almost) for Free

Matthias Hein, Alexander Meinke, Julian Bitterwolf, 2021, NeurIPS.

Numerical evolution of axisymmetric, isolated systems in general relativity

Matthias Hein, J. Frauendiener, 2002, gr-qc/0207094.

Diffusion Visual Counterfactual Explanations

Matthias Hein, Francesco Croce, Maximilian Augustin, 2022, NeurIPS.

Provably Adversarially Robust Detection of Out-of-Distribution Data (Almost) for Free

Matthias Hein, Alexander Meinke, Julian Bitterwolf, 2022, NeurIPS.

Sound Randomized Smoothing in Floating-Point Arithmetics

Matthias Hein, Václav Voráček, 2022, ArXiv.

Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core Quantities

Matthias Hein, Alexander Meinke, Maximilian Augustin, 2022, ICML.

On the interplay of adversarial robustness and architecture components: patches, convolution and attention

Matthias Hein, Francesco Croce, 2022, ArXiv.

Classifiers Should Do Well Even on Their Worst Classes

Matthias Hein, Alexander Meinke, Julian Bitterwolf, 2022 .

Lost in Translation: Modern Image Classifiers still degrade even under simple Translations

Matthias Hein, Leander Kurscheidt, 2022 .

Spurious Features Everywhere - Large-Scale Detection of Harmful Spurious Features in ImageNet

Matthias Hein, Maximilian Augustin, Valentyn Boreiko, 2022, ArXiv.

Neural Networks Should Be Wide Enough to Learn Disconnected Decision Regions

Matthias Hein, Quynh Nguyen, Mahesh Chandra Mukkamala, 2018, ICML.

Optimization Landscape and Expressivity of Deep CNNs

Matthias Hein, Quynh Nguyen, Matthias Hein, 2017, ICML.

The loss surface and expressivity of deep convolutional neural networks pdf

Matthias Hein, Quynh Nguyen, Matthias Hein, 2017, ICLR.

HE LOSS SURFACE AND EXPRESSIVITY OF DEEP CONVOLUTIONAL NEURAL NETWORKS

Matthias Hein, Quynh N. Nguyen, 2018 .

T HE LOSS SURFACE AND EXPRESSIVITY OF DEEP CONVOLUTIONAL NEURAL NETWORKS

Matthias Hein, Quynh N. Nguyen, 2017 .

Error Estimates for Spectral Convergence of the Graph Laplacian on Random Geometric Graphs Toward the Laplace–Beltrami Operator

Matthias Hein, Dejan Slepcev, Nicolás García Trillos, 2018, Found. Comput. Math..

Graph Laplacians and their Convergence on Random Neighborhood Graphs pdf

Ulrike von Luxburg, Matthias Hein, Jean-Yves Audibert, 2006, J. Mach. Learn. Res..

Manifold Denoising

Matthias Hein, Markus Maier, Matthias Hein, 2006, NIPS.

From Graphs to Manifolds - Weak and Strong Pointwise Consistency of Graph Laplacians

Ulrike von Luxburg, Matthias Hein, Jean-Yves Audibert, 2005, COLT.

Scaling up the Randomized Gradient-Free Adversarial Attack Reveals Overestimation of Robustness Using Established Attacks

Matthias Hein, Jonas Rauber, Francesco Croce, 2019, International Journal of Computer Vision.

Spectral Clustering of Signed Graphs via Matrix Power Means

Matthias Hein, Francesco Tudisco, Pedro Mercado, 2019, ICML.

Clustering Signed Networks with the Geometric Mean of Laplacians

Matthias Hein, Pedro Mercado, Francesco Tudisco, 2016, NIPS.

Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks

Matthias Hein, Francesco Croce, Matthias Hein, 2020, ICML.

Community detection in networks via nonlinear modularity eigenvectors pdf

Matthias Hein, Pedro Mercado, Francesco Tudisco, 2017, SIAM J. Appl. Math..

Constrained fractional set programs and their application in local clustering and community detection pdf

Matthias Hein, Syama Sundar Rangapuram, Thomas Bühler, 2013, ICML.

Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks

Matthias Hein, Maksym Andriushchenko, Matthias Hein, 2019, NeurIPS.

MeDeCom: discovery and quantification of latent components of heterogeneous methylomes

Matthias Hein, Nikita Vedeneev, Jörn Walter, 2017, Genome Biology.

Semi-supervised Regression using Hessian energy with an application to semi-supervised dimensionality reduction

Florian Steinke, Matthias Hein, Kwang In Kim, 2009, NIPS.

Analysis and Optimization of Loss Functions for Multiclass, Top-k, and Multilabel Classification

Bernt Schiele, Matthias Hein, Maksim Lapin, 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Loss Functions for Top-k Error: Analysis and Insights

Bernt Schiele, Matthias Hein, Maksim Lapin, 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

A nodal domain theorem and a higher-order Cheeger inequality for the graph $p$-Laplacian

Matthias Hein, Francesco Tudisco, Matthias Hein, 2016, Journal of Spectral Theory.

How the result of graph clustering methods depends on the construction of the graph pdf

Ulrike von Luxburg, Matthias Hein, Markus Maier, 2011, ArXiv.

Influence of graph construction on graph-based clustering measures

Ulrike von Luxburg, Matthias Hein, Markus Maier, 2008, NIPS.

Intrinsic dimensionality estimation of submanifolds in Rd

Matthias Hein, Jean-Yves Audibert, Jean-Yves Audibert, 2005, ICML.

Large-scale antibody profiling of human blood sera: The future of molecular diagnosis

Eckart Meese, Andreas Keller, Joachim Weickert, 2009, Informatik-Spektrum.

Simple Does It: Weakly Supervised Instance and Semantic Segmentation

Bernt Schiele, Matthias Hein, Rodrigo Benenson, 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks

Philipp Hennig, Matthias Hein, Agustinus Kristiadi, 2020, ICML.

Latent Embeddings for Zero-Shot Classification

Bernt Schiele, Yongqin Xian, Zeynep Akata, 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Disentangling Adversarial Robustness and Generalization

Bernt Schiele, Matthias Hein, David Stutz, 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Computing the norm of nonnegative matrices and the log-Sobolev constant of Markov chains pdf

Antoine Gautier, Francesco Tudisco, Matthias Hein, 2020, ArXiv.

Nonparametric Regression between General Riemannian Manifolds

Bernhard Schölkopf, Florian Steinke, Matthias Hein, 2010, SIAM J. Imaging Sci..

Sound Randomized Smoothing in Floating-Point Arithmetics

Matthias Hein, Václav Voráček, 2022, ArXiv.

Provably Adversarially Robust Nearest Prototype Classifiers

Matthias Hein, Václav Voráček, 2022, ICML.

Provable Robustness of ReLU networks via Maximization of Linear Regions pdf

Matthias Hein, Francesco Croce, Maksym Andriushchenko, 2018, AISTATS.

Provable robustness against all adversarial lp-perturbations for p≥1 pdf

Matthias Hein, Francesco Croce, Matthias Hein, 2019, ArXiv.

The Total Variation on Hypergraphs - Learning on Hypergraphs Revisited

Matthias Hein, Syama Sundar Rangapuram, Simon Setzer, 2013, NIPS.

Certifiably Adversarially Robust Detection of Out-of-Distribution Data

Matthias Hein, Julian Bitterwolf, Alexander Meinke, 2020, NeurIPS.

Why ReLU Networks Yield High-Confidence Predictions Far Away From the Training Data and How to Mitigate the Problem

Matthias Hein, Julian Bitterwolf, Maksym Andriushchenko, 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Relating Adversarially Robust Generalization to Flat Minima

B. Schiele, Matthias Hein, David Stutz, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).

K2S Challenge: From Undersampled K-Space to Automatic Segmentation

Christian F. Baumgartner, Aniket A. Tolpadi, Quintin van Lohuizen, 2023, Bioengineering.

An Efficient Multilinear Optimization Framework for Hypergraph Matching

Quynh N. Nguyen, Matthias Hein, Antoine Gautier, 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack

Matthias Hein, Francesco Croce, Matthias Hein, 2019, ICML.

Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial Attacks pdf

Matthias Hein, Jan Hendrik Metzen, Maksym Yatsura, 2021, NeurIPS.

Square Attack: a query-efficient black-box adversarial attack via random search

Nicolas Flammarion, Matthias Hein, Maksym Andriushchenko, 2019, ECCV.

Towards neural networks that provably know when they don't know

Matthias Hein, Alexander Meinke, 2019, ICLR.

Confidence-Calibrated Adversarial Training: Generalizing to Unseen Attacks

Bernt Schiele, David Stutz, Matthias Hein, 2019, ICML.

Learning using privileged information: SV M+ and weighted SVM

Bernt Schiele, Matthias Hein, Maksim Lapin, 2013, Neural Networks.

Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks pdf

Nicolas Flammarion, Matthias Hein, Francesco Croce, 2020, AAAI.

Sparse and Imperceivable Adversarial Attacks

Matthias Hein, Francesco Croce, Matthias Hein, 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

Revisiting Adversarial Training for ImageNet: Architectures, Training and Generalization across Threat Models

Naman D. Singh, Matthias Hein, Francesco Croce, 2023, ArXiv.

Certified Defences Against Adversarial Patch Attacks on Semantic Segmentation

J. H. Metzen, Matthias Hein, K. Sakmann, 2022, ICLR.

Logit Pairing Methods Can Fool Gradient-Based Attacks pdf

Matthias Hein, Dietrich Klakow, Thomas Alexander Trost, 2018, ArXiv.

Classifier based graph construction for video segmentation

Bernt Schiele, Matthias Hein, Anna Khoreva, 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Generalized Matrix Means for Semi-Supervised Learning with Multilayer Graphs

Francesco Tudisco, Matthias Hein, Pedro Mercado, 2019, NeurIPS.

The Power Mean Laplacian for Multilayer Graph Clustering

Matthias Hein, Pedro Mercado, Antoine Gautier, 2018, AISTATS.

On the loss landscape of a class of deep neural networks with no bad local valleys pdf

Matthias Hein, Quynh Nguyen, Mahesh Chandra Mukkamala, 2018, ICLR.

Random and Adversarial Bit Error Robustness: Energy-Efficient and Secure DNN Accelerators

B. Schiele, Matthias Hein, David Stutz, 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Bit Error Robustness for Energy-Efficient DNN Accelerators

Bernt Schiele, David Stutz, Nandhini Chandramoorthy, 2020, MLSys.

On Mitigating Random and Adversarial Bit Errors pdf

Bernt Schiele, David Stutz, Nandhini Chandramoorthy, 2020, ArXiv.

Normalization Layers Are All That Sharpness-Aware Minimization Needs

D. Rolnick, Matthias Hein, Maximilian Mueller, 2023, NeurIPS.

Robust Nonparametric Regression with Metric-Space Valued Output

Matthias Hein, Matthias Hein, 2009, NIPS.

A modern look at the relationship between sharpness and generalization

Nicolas Flammarion, Matthias Hein, Maksym Andriushchenko, 2023, ICML.

Supplementary Material for Relating Adversarially Robust Generalization to Flat Minima

B. Schiele, Matthias Hein, David Stutz, 2021 .

Hitting and commute times in large graphs are often misleading

Ulrike von Luxburg, Matthias Hein, Agnes Radl, 2010, 1003.1266.

Getting lost in space: Large sample analysis of the resistance distance

Ulrike von Luxburg, Matthias Hein, Agnes Radl, 2010, NIPS.

An Inverse Power Method for Nonlinear Eigenproblems with Applications in 1-Spectral Clustering and Sparse PCA

Matthias Hein, Thomas Bühler, Matthias Hein, 2010, NIPS.

Mind the box: l1-APGD for sparse adversarial attacks on image classifiers

Matthias Hein, Francesco Croce, 2021, ICML.

Being a Bit Frequentist Improves Bayesian Neural Networks

Philipp Hennig, Matthias Hein, Agustinus Kristiadi, 2021, AISTATS.

Provable Worst Case Guarantees for the Detection of Out-of-Distribution Data pdf

Matthias Hein, Julian Bitterwolf, Alexander Meinke, 2020, ArXiv.

Learnable Uncertainty under Laplace Approximations

Matthias Hein, Agustinus Kristiadi, Philipp Hennig, 2020, UAI.

Manifold Denoising as Preprocessing for Finding Natural Representations of Data

Matthias Hein, Markus Maier, Matthias Hein, 2007, AAAI.

Isotope pattern deconvolution for peptide mass spectrometry by non-negative least squares/least absolute deviation template matching

Matthias Hein, Andreas Hildebrandt, Andreas Tholey, 2012, BMC Bioinformatics.

Globally Optimal Training of Generalized Polynomial Neural Networks with Nonlinear Spectral Methods

Quynh N. Nguyen, Matthias Hein, Antoine Gautier, 2016, NIPS.

Robust PCA: Optimization of the Robust Reconstruction Error Over the Stiefel Manifold pdf

Matthias Hein, Simon Setzer, Anastasia Podosinnikova, 2014, GCPR.

Nonlinear Perron-Frobenius Theorems for Nonnegative Tensors

Matthias Hein, A. Gautier, Francesco Tudisco, 2023, SIAM Rev..

Correction of noisy labels via mutual consistency check

Matthias Hein, Sahely Bhadra, Matthias Hein, 2015, Neurocomputing.

An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence

Philipp Hennig, Matthias Hein, Agustinus Kristiadi, 2020, NeurIPS.

Robust Semantic Segmentation: Strong Adversarial Attacks and Fast Training of Robust Models

Naman D. Singh, Matthias Hein, Francesco Croce, 2023, arXiv.org.

A randomized gradient-free attack on ReLU networks

Matthias Hein, Francesco Croce, Matthias Hein, 2018, GCPR.

Non-parametric Regression Between Manifolds

Florian Steinke, Matthias Hein, Matthias Hein, 2008, NIPS.

Sparse recovery by thresholded non-negative least squares

Matthias Hein, Martin Slawski, Matthias Hein, 2011, NIPS.

An integer linear programming approach for finding deregulated subgraphs in regulatory networks

Matthias Hein, Hans-Peter Lenhof, D. Maisel, 2011, Nucleic acids research.

Enhancement of Bright Video Features for HDR Displays

Hans-Peter Seidel, Matthias Hein, Rafal Mantiuk, 2008 .

Scalable Multitask Representation Learning for Scene Classification

Bernt Schiele, Matthias Hein, Maksim Lapin, 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

Hitting and commute times in large random neighborhood graphs

Ulrike von Luxburg, Matthias Hein, Agnes Radl, 2014, J. Mach. Learn. Res..

Towards realistic team formation in social networks based on densest subgraphs

Matthias Hein, Syama Sundar Rangapuram, Thomas Bühler, 2013, WWW.

Neural Network Heuristic Functions: Taking Confidence into Account

Matthias Hein, Julian Bitterwolf, Patrick Ferber, 2022, SOCS.

On the Adversarial Robustness of Multi-Modal Foundation Models

Matthias Hein, Christian Schlarmann, 2023, arXiv.org.

Fixing Asymptotic Uncertainty of Bayesian Neural Networks with Infinite ReLU Features pdf

Agustinus Kristiadi, Philipp Hennig, Matthias Hein, 2020, ArXiv.

A flexible tensor block coordinate ascent scheme for hypergraph matching

Matthias Hein, Antoine Gautier, Quynh Nguyen Ngoc, 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

S OUND R ANDOMIZED S MOOTHING IN F LOATING - P OINT A RITHMETIC

Matthias Hein, 2023 .

Tight Continuous Relaxation of the Balanced k-Cut Problem

Matthias Hein, Syama Sundar Rangapuram, Pramod Kaushik Mudrakarta, 2014, NIPS.

Beyond Spectral Clustering - Tight Relaxations of Balanced Graph Cuts

Matthias Hein, Simon Setzer, Matthias Hein, 2011, NIPS.

Robust Principal Component Analysis as a Nonlinear Eigenproblem

Matthias Hein, Rainer Gemulla, Anastasia Podosinnikova, 2013 .

Regularization-Free Estimation in Trace Regression with Symmetric Positive Semidefinite Matrices

Matthias Hein, Ping Li, Martin Slawski, 2015, NIPS.

Out-distribution aware Self-training in an Open World Setting pdf

Matthias Hein, Maximilian Augustin, Matthias Hein, 2020, ArXiv.

Analysis and Optimization of Loss Functions for Multiclass, Top-k, and Multilabel Classification

B. Schiele, Matthias Hein, Maksim Lapin, 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

A nodal domain theorem and a higher-order Cheeger inequality for the graph $p$-Laplacian

Matthias Hein, Francesco Tudisco, 2016, Journal of Spectral Theory.

Weakly Supervised Object Boundaries

Bernt Schiele, Matthias Hein, Rodrigo Benenson, 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Spurious Features Everywhere - Large-Scale Detection of Harmful Spurious Features in ImageNet

Matthias Hein, Maximilian Augustin, Valentyn Boreiko, 2022, 2023 IEEE/CVF International Conference on Computer Vision (ICCV).

A unifying Perron-Frobenius theorem for nonnegative tensors via multi-homogeneous maps pdf

Matthias Hein, Antoine Gautier, Francesco Tudisco, 2018, SIAM J. Matrix Anal. Appl..

Revisiting Adversarial Training for ImageNet: Architectures, Training and Generalization across Threat Models

Naman D. Singh, Matthias Hein, Francesco Croce, 2023, ArXiv.

Provable robustness against all adversarial $l_p$-perturbations for $p\geq 1$

Francesco Croce, Matthias Hein, Matthias Hein, 2019, ICLR.

Enhancement of Bright Video Features for HDR Displays

Rafał K. Mantiuk, H. Seidel, Matthias Hein, 2008, Comput. Graph. Forum.

In or Out? Fixing ImageNet Out-of-Distribution Detection Evaluation

Matthias Hein, Julian Bitterwolf, Maximilian Mueller, 2023, ICML.

The Total Variation on Hypergraphs - Learning on Hypergraphs Revisited

Syama Sundar Rangapuram, Matthias Hein, S. Setzer, 2013, NIPS.

Spectral clustering based on the graph p-Laplacian

Matthias Hein, Thomas Bühler, 2009, ICML '09.

Confidence-Calibrated Adversarial Training: Generalizing to Unseen Attacks

B. Schiele, Matthias Hein, David Stutz, 2019, ICML.

Bit Error Robustness for Energy-Efficient DNN Accelerators

B. Schiele, Matthias Hein, David Stutz, 2020, MLSys.

The Perron-Frobenius Theorem for Multihomogeneous Mappings

Matthias Hein, Antoine Gautier, Francesco Tudisco, 2017, SIAM J. Matrix Anal. Appl..

Cluster Identification in Nearest-Neighbor Graphs

Ulrike von Luxburg, Matthias Hein, Markus Maier, 2007, ALT.