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ORCIDStefan Harmeling
Person information
- affiliation: Technische Universität Dortmund, Dortmund, Germany
- affiliation (former): Heinrich Heine University of Düsseldorf, Computer Science Department, Germany
- affiliation (former): Max Planck Institute for Intelligent Systems, Tübinngen, Germany
- affiliation (former): Fraunhofer FIRST, Berlin, Germany
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2020 – today
- 2025
[j22]Tobias Uelwer, Jan Robine, Stefan Sylvius Wagner, Marc Höftmann, Eric Upschulte, Sebastian Konietzny, Maike Behrendt, Stefan Harmeling:
A survey on self-supervised methods for visual representation learning. Mach. Learn. 114(4): 111 (2025)- [c41]Jan Robine, Marc Höftmann, Stefan Harmeling:
Simple, Good, Fast: Self-Supervised World Models Free of Baggage. ICLR 2025 - [c40]Stefan Sylvius Wagner, Maike Behrendt, Marc Ziegele, Stefan Harmeling:
The Power of LLM-Generated Synthetic Data for Stance Detection in Online Political Discussions. ICLR 2025 - [i38]Stefan Sylvius Wagner, Stefan Harmeling:
Object-Aware DINO (Oh-A-Dino): Enhancing Self-Supervised Representations for Multi-Object Instance Retrieval. CoRR abs/2503.09867 (2025) - [i37]Maike Behrendt, Stefan Sylvius Wagner, Stefan Harmeling:
MaxPoolBERT: Enhancing BERT Classification via Layer- and Token-Wise Aggregation. CoRR abs/2505.15696 (2025) - [i36]Maike Behrendt, Stefan Sylvius Wagner, Carina Weinmann, Marike Bormann, Mira Warne, Stefan Harmeling:
Natural Language Processing to Enhance Deliberation in Political Online Discussions: A Survey. CoRR abs/2506.02533 (2025) - [i35]Jan Robine, Marc Höftmann, Stefan Harmeling:
Simple, Good, Fast: Self-Supervised World Models Free of Baggage. CoRR abs/2506.02612 (2025) - [i34]Jonas Kirch, Sebastian Becker, Tiago Koketsu Rodrigues, Stefan Harmeling:
DFCA: Decentralized Federated Clustering Algorithm. CoRR abs/2510.15300 (2025) - 2024
- [c39]Stefan Sylvius Wagner, Stefan Harmeling:
Just Cluster It: An Approach for Exploration in High-Dimensions using Clustering and Pre-Trained Representations. ICML 2024: 49788-49807 - [i33]Stefan Sylvius Wagner, Stefan Harmeling:
Just Cluster It: An Approach for Exploration in High-Dimensions using Clustering and Pre-Trained Representations. CoRR abs/2402.03138 (2024) - [i32]Maike Behrendt, Stefan Sylvius Wagner, Marc Ziegele, Lena Wilms, Anke Stoll, Dominique Heinbach, Stefan Harmeling:
AQuA - Combining Experts' and Non-Experts' Views To Assess Deliberation Quality in Online Discussions Using LLMs. CoRR abs/2404.02761 (2024) - [i31]Stefan Sylvius Wagner, Maike Behrendt, Marc Ziegele, Stefan Harmeling:
SQBC: Active Learning using LLM-Generated Synthetic Data for Stance Detection in Online Political Discussions. CoRR abs/2404.08078 (2024) - [i30]Stefan Sylvius Wagner, Maike Behrendt, Marc Ziegele, Stefan Harmeling:
The Power of LLM-Generated Synthetic Data for Stance Detection in Online Political Discussions. CoRR abs/2406.12480 (2024) - [i29]Maike Behrendt, Stefan Sylvius Wagner, Stefan Harmeling:
Supporting Online Discussions: Integrating AI Into the adhocracy+ Participation Platform To Enhance Deliberation. CoRR abs/2409.07780 (2024) - 2023
- [j21]Maximilian Kertel
, Stefan Harmeling, Markus Pauly, Nadja Klein:
Learning Causal Graphs in Manufacturing Domains Using Structural Equation Models. Int. J. Semantic Comput. 17(4): 511-528 (2023) - [j20]Tobias Uelwer, Sebastian Konietzny, Alexander Oberstraß, Stefan Harmeling:
Learning Conditional Generative Models for Phase Retrieval. J. Mach. Learn. Res. 24: 332:1-332:28 (2023) - [j19]Jan Robine, Tobias Uelwer, Stefan Harmeling:
Smaller World Models for Reinforcement Learning. Neural Process. Lett. 55(8): 11397-11427 (2023) - [j18]Stefan Sylvius Wagner, Peter Arndt, Jan Robine, Stefan Harmeling:
Cyclophobic Reinforcement Learning. Trans. Mach. Learn. Res. 2023 (2023) - [c38]Jan Robine, Marc Höftmann, Tobias Uelwer, Stefan Harmeling:
Transformer-based World Models Are Happy With 100k Interactions. ICLR 2023 - [c37]Hendryk Weiland, Maike Behrendt, Stefan Harmeling:
Automatic Dictionary Generation: Could Brothers Grimm Create a Dictionary with BERT? KONVENS 2023: 102-120 - [i28]Marc Höftmann, Jan Robine, Stefan Harmeling:
Time-Myopic Go-Explore: Learning A State Representation for the Go-Explore Paradigm. CoRR abs/2301.05635 (2023) - [i27]Jan Robine, Marc Höftmann, Tobias Uelwer, Stefan Harmeling:
Transformer-based World Models Are Happy With 100k Interactions. CoRR abs/2303.07109 (2023) - [i26]Tobias Uelwer, Jan Robine, Stefan Sylvius Wagner, Marc Höftmann, Eric Upschulte, Sebastian Konietzny, Maike Behrendt, Stefan Harmeling:
A Survey on Self-Supervised Representation Learning. CoRR abs/2308.11455 (2023) - [i25]Stefan Sylvius Wagner, Peter Arndt, Jan Robine, Stefan Harmeling:
Cyclophobic Reinforcement Learning. CoRR abs/2308.15911 (2023) - [i24]Thomas Germer, Jan Robine, Sebastian Konietzny, Stefan Harmeling, Tobias Uelwer:
Limited-Angle Tomography Reconstruction via Deep End-To-End Learning on Synthetic Data. CoRR abs/2309.06948 (2023) - [i23]Marc Höftmann, Jan Robine, Stefan Harmeling:
Backward Learning for Goal-Conditioned Policies. CoRR abs/2312.05044 (2023) - 2022
- [j17]Eric Upschulte, Stefan Harmeling, Katrin Amunts, Timo Dickscheid:
Contour proposal networks for biomedical instance segmentation. Medical Image Anal. 77: 102371 (2022) - [j16]Tobias Uelwer, Sebastian Konietzny, Stefan Harmeling:
Optimizing Intermediate Representations of Generative Models for Phase Retrieval. Trans. Mach. Learn. Res. 2022 (2022) - [c36]Maximilian Kertel, Stefan Harmeling, Markus Pauly:
Learning Causal Graphs in Manufacturing Domains using Structural Equation Models. AI4I 2022: 14-19 - [c35]Eric Upschulte, Stefan Harmeling, Katrin Amunts, Timo Dickscheid:
Uncertainty-Aware Contour Proposal Networks for Cell Segmentation in Multi-Modality High-Resolution Microscopy Images. Cell Segmentation Challenge @ NeurIPS 2022: 1-12 - [i22]Thomas Germer, Tobias Uelwer, Stefan Harmeling:
Deblurring Photographs of Characters Using Deep Neural Networks. CoRR abs/2205.15053 (2022) - [i21]Tobias Uelwer, Sebastian Konietzny, Stefan Harmeling:
Optimizing Intermediate Representations of Generative Models for Phase Retrieval. CoRR abs/2205.15617 (2022) - [i20]Leonid Kostrykin, Stefan Harmeling:
Blindly Deconvolving Super-noisy Blurry Image Sequences. CoRR abs/2210.00252 (2022) - [i19]Maximilian Kertel, Stefan Harmeling, Markus Pauly:
Learning Causal Graphs in Manufacturing Domains using Structural Equation Models. CoRR abs/2210.14573 (2022) - 2021
- [j15]Christian Schiffer, Hannah Spitzer, Kai Kiwitz, Nina Unger, Konrad Wagstyl, Alan C. Evans, Stefan Harmeling, Katrin Amunts, Timo Dickscheid:
Convolutional neural networks for cytoarchitectonic brain mapping at large scale. NeuroImage 240: 118327 (2021) - [c34]Tobias Uelwer, Tobias Hoffmann, Stefan Harmeling:
Non-iterative Phase Retrieval with Cascaded Neural Networks. ICANN (2) 2021: 295-306 - [c33]Stefan Wagner, Michael Janschek, Tobias Uelwer, Stefan Harmeling:
Learning to Plan via a Multi-step Policy Regression Method. ICANN (4) 2021: 481-492 - [c32]Christian Schiffer, Katrin Amunts, Stefan Harmeling, Timo Dickscheid:
Contrastive Representation Learning For Whole Brain Cytoarchitectonic Mapping In Histological Human Brain Sections. ISBI 2021: 603-606 - [c31]Christian Schiffer, Stefan Harmeling, Katrin Amunts, Timo Dickscheid:
2D Histology Meets 3D Topology: Cytoarchitectonic Brain Mapping with Graph Neural Networks. MICCAI (8) 2021: 395-404 - [c30]Markus Brenneis, Maike Behrendt, Stefan Harmeling:
How Will I Argue? A Dataset for Evaluating Recommender Systems for Argumentations. SIGDIAL 2021: 360-367 - [i18]Christian Schiffer, Stefan Harmeling, Katrin Amunts, Timo Dickscheid:
2D histology meets 3D topology: Cytoarchitectonic brain mapping with Graph Neural Networks. CoRR abs/2103.05259 (2021) - [i17]Eric Upschulte, Stefan Harmeling, Katrin Amunts, Timo Dickscheid:
Contour Proposal Networks for Biomedical Instance Segmentation. CoRR abs/2104.03393 (2021) - [i16]Stefan Wagner, Michael Janschek, Tobias Uelwer, Stefan Harmeling:
Learning to Plan via a Multi-Step Policy Regression Method. CoRR abs/2106.10075 (2021) - [i15]Tobias Uelwer, Tobias Hoffmann, Stefan Harmeling:
Non-Iterative Phase Retrieval With Cascaded Neural Networks. CoRR abs/2106.10195 (2021) - [i14]Tobias Uelwer, Nick Rucks, Stefan Harmeling:
A Closer Look at Reference Learning for Fourier Phase Retrieval. CoRR abs/2110.13688 (2021) - 2020
- [j14]Thomas Germer, Tobias Uelwer, Stefan Conrad, Stefan Harmeling:
PyMatting: A Python Library for Alpha Matting. J. Open Source Softw. 5(54): 2481 (2020) - [c29]Felix Dangel, Stefan Harmeling, Philipp Hennig:
Modular Block-diagonal Curvature Approximations for Feedforward Architectures. AISTATS 2020: 799-808 - [c28]Markus Brenneis, Maike Behrendt, Stefan Harmeling, Martin Mauve:
How Much Do I Argue Like You? Towards a Metric on Weighted Argumentation Graphs. SAFA 2020: 2-13 - [c27]Tobias Uelwer, Alexander Oberstraß, Stefan Harmeling:
Phase Retrieval Using Conditional Generative Adversarial Networks. ICPR 2020: 731-738 - [c26]Thomas Germer, Tobias Uelwer, Stefan Conrad, Stefan Harmeling:
Fast Multi-Level Foreground Estimation. ICPR 2020: 1104-1111 - [i13]Thomas Germer, Tobias Uelwer, Stefan Conrad, Stefan Harmeling:
PyMatting: A Python Library for Alpha Matting. CoRR abs/2003.12382 (2020) - [i12]Thomas Germer, Tobias Uelwer, Stefan Conrad, Stefan Harmeling:
Fast Multi-Level Foreground Estimation. CoRR abs/2006.14970 (2020) - [i11]Jan Robine, Tobias Uelwer, Stefan Harmeling:
Discrete Latent Space World Models for Reinforcement Learning. CoRR abs/2010.05767 (2020) - [i10]Christian Schiffer, Hannah Spitzer, Kai Kiwitz, Nina Unger, Konrad Wagstyl, Alan C. Evans, Stefan Harmeling, Katrin Amunts, Timo Dickscheid:
Convolutional Neural Networks for cytoarchitectonic brain mapping at large scale. CoRR abs/2011.12857 (2020) - [i9]Christian Schiffer, Katrin Amunts, Stefan Harmeling, Timo Dickscheid:
Contrastive Representation Learning for Whole Brain Cytoarchitectonic Mapping in Histological Human Brain Sections. CoRR abs/2011.12865 (2020)
2010 – 2019
- 2019
- [i8]Felix Michels, Tobias Uelwer, Eric Upschulte, Stefan Harmeling:
On the Vulnerability of Capsule Networks to Adversarial Attacks. CoRR abs/1906.03612 (2019) - [i7]Tobias Uelwer, Alexander Oberstraß, Stefan Harmeling:
Phase Retrieval using Conditional Generative Adversarial Networks. CoRR abs/1912.04981 (2019) - 2018
- [c25]Hannah Spitzer, Kai Kiwitz, Katrin Amunts, Stefan Harmeling, Timo Dickscheid:
Improving Cytoarchitectonic Segmentation of Human Brain Areas with Self-supervised Siamese Networks. MICCAI (3) 2018: 663-671 - [i6]Hannah Spitzer, Kai Kiwitz, Katrin Amunts, Stefan Harmeling, Timo Dickscheid:
Improving Cytoarchitectonic Segmentation of Human Brain Areas with Self-supervised Siamese Networks. CoRR abs/1806.05104 (2018) - 2017
- [c24]Hannah Spitzer, Katrin Amunts, Stefan Harmeling, Timo Dickscheid:
Parcellation of visual cortex on high-resolution histological brain sections using convolutional neural networks. ISBI 2017: 920-923 - [i5]Hannah Spitzer, Katrin Amunts, Stefan Harmeling, Timo Dickscheid:
Parcellation of Visual Cortex on high-resolution histological Brain Sections using Convolutional Neural Networks. CoRR abs/1705.10545 (2017) - 2016
- [j13]Christian J. Schuler, Michael Hirsch, Stefan Harmeling, Bernhard Schölkopf:
Learning to Deblur. IEEE Trans. Pattern Anal. Mach. Intell. 38(7): 1439-1451 (2016) - 2015
- [j12]Bernhard Schölkopf, Krikamol Muandet, Kenji Fukumizu, Stefan Harmeling, Jonas Peters:
Computing functions of random variables via reproducing kernel Hilbert space representations. Stat. Comput. 25(4): 755-766 (2015) - 2014
- [j11]Christoph H. Lampert, Hannes Nickisch, Stefan Harmeling:
Attribute-Based Classification for Zero-Shot Visual Object Categorization. IEEE Trans. Pattern Anal. Mach. Intell. 36(3): 453-465 (2014) - [c23]Rolf Köhler, Christian J. Schuler, Bernhard Schölkopf, Stefan Harmeling:
Mask-Specific Inpainting with Deep Neural Networks. GCPR 2014: 523-534 - [i4]Christian J. Schuler, Michael Hirsch, Stefan Harmeling, Bernhard Schölkopf:
Learning to Deblur. CoRR abs/1406.7444 (2014) - 2013
- [j10]Jakob Zscheischler, Miguel D. Mahecha, Stefan Harmeling, Markus Reichstein:
Detection and attribution of large spatiotemporal extreme events in Earth observation data. Ecol. Informatics 15: 66-73 (2013) - [c22]Christian J. Schuler, Harold Christopher Burger, Stefan Harmeling, Bernhard Schölkopf:
A Machine Learning Approach for Non-blind Image Deconvolution. CVPR 2013: 1067-1074 - [c21]Stefan Harmeling, Michael Hirsch, Bernhard Schölkopf:
On a Link Between Kernel Mean Maps and Fraunhofer Diffraction, with an Application to Super-Resolution Beyond the Diffraction Limit. CVPR 2013: 1083-1090 - [c20]Harold Christopher Burger, Christian J. Schuler, Stefan Harmeling:
Learning How to Combine Internal and External Denoising Methods. GCPR 2013: 121-130 - [c19]Rolf Köhler, Michael Hirsch, Bernhard Schölkopf, Stefan Harmeling:
Improving alpha matting and motion blurred foreground estimation. ICIP 2013: 3446-3450 - [c18]Moritz Grosse-Wentrup, Stefan Harmeling, Thorsten O. Zander, N. Jeremy Hill, Bernhard Schölkopf:
How to Test the Quality of Reconstructed Sources in Independent Component Analysis (ICA) of EEG/MEG Data. PRNI 2013: 102-105 - 2012
- [c17]Harold Christopher Burger, Christian J. Schuler, Stefan Harmeling:
Image denoising: Can plain neural networks compete with BM3D? CVPR 2012: 2392-2399 - [c16]Rolf Köhler, Michael Hirsch, Betty J. Mohler, Bernhard Schölkopf, Stefan Harmeling:
Recording and Playback of Camera Shake: Benchmarking Blind Deconvolution with a Real-World Database. ECCV (7) 2012: 27-40 - [c15]Christian J. Schuler, Michael Hirsch, Stefan Harmeling, Bernhard Schölkopf:
Blind Correction of Optical Aberrations. ECCV (3) 2012: 187-200 - [c14]Jakob Zscheischler, Miguel D. Mahecha, Stefan Harmeling:
Climate Classifications: the Value of Unsupervised Clustering. ICCS 2012: 897-906 - [i3]Harold Christopher Burger, Christian J. Schuler, Stefan Harmeling:
Image denoising with multi-layer perceptrons, part 1: comparison with existing algorithms and with bounds. CoRR abs/1211.1544 (2012) - [i2]Harold Christopher Burger, Christian J. Schuler, Stefan Harmeling:
Image denoising with multi-layer perceptrons, part 2: training trade-offs and analysis of their mechanisms. CoRR abs/1211.1552 (2012) - 2011
- [j9]Stefan Harmeling, Christopher K. I. Williams:
Greedy Learning of Binary Latent Trees. IEEE Trans. Pattern Anal. Mach. Intell. 33(6): 1087-1097 (2011) - [c13]Harold Christopher Burger, Stefan Harmeling:
Improving Denoising Algorithms via a Multi-scale Meta-procedure. DAGM-Symposium 2011: 206-215 - [c12]Harold Christopher Burger, Bernhard Schölkopf, Stefan Harmeling:
Removing noise from astronomical images using a pixel-specific noise model. ICCP 2011: 1-8 - [c11]Michael Hirsch, Christian J. Schuler, Stefan Harmeling, Bernhard Schölkopf:
Fast removal of non-uniform camera shake. ICCV 2011: 463-470 - [c10]Christian J. Schuler, Michael Hirsch, Stefan Harmeling, Bernhard Schölkopf:
Non-stationary correction of optical aberrations. ICCV 2011: 659-666 - [c9]Alexander Loktyushin, Stefan Harmeling:
Automatic foreground-background refocusing. ICIP 2011: 3445-3448 - 2010
- [j8]David Baehrens, Timon Schroeter, Stefan Harmeling, Motoaki Kawanabe, Katja Hansen, Klaus-Robert Müller:
How to Explain Individual Classification Decisions. J. Mach. Learn. Res. 11: 1803-1831 (2010) - [c8]Michael Hirsch, Suvrit Sra, Bernhard Schölkopf, Stefan Harmeling:
Efficient filter flow for space-variant multiframe blind deconvolution. CVPR 2010: 607-614 - [c7]Stefan Harmeling, Suvrit Sra, Michael Hirsch, Bernhard Schölkopf:
Multiframe blind deconvolution, super-resolution, and saturation correction via incremental EM. ICIP 2010: 3313-3316 - [c6]Stefan Harmeling, Michael Hirsch, Bernhard Schölkopf:
Space-Variant Single-Image Blind Deconvolution for Removing Camera Shake. NIPS 2010: 829-837
2000 – 2009
- 2009
- [j7]Stefan Harmeling:
Inferring textual entailment with a probabilistically sound calculus. Nat. Lang. Eng. 15(4): 459-477 (2009) - [c5]Christoph H. Lampert, Hannes Nickisch, Stefan Harmeling:
Learning to detect unseen object classes by between-class attribute transfer. CVPR 2009: 951-958 - [i1]David Baehrens, Timon Schroeter, Stefan Harmeling, Motoaki Kawanabe, Katja Hansen, Klaus-Robert Müller:
How to Explain Individual Classification Decisions. CoRR abs/0912.1128 (2009) - 2007
- [c4]Stefan Harmeling:
An Extensible Probabilistic Transformation-based Approach to the Third Recognizing Textual Entailment Challenge. ACL-PASCAL@ACL 2007: 137-142 - 2006
- [j6]Stefan Harmeling, Guido Dornhege, David M. J. Tax, Frank C. Meinecke, Klaus-Robert Müller:
From outliers to prototypes: Ordering data. Neurocomputing 69(13-15): 1608-1618 (2006) - 2005
- [b1]Stefan Harmeling:
Independent component analysis and beyond. University of Potsdam, Germany, 2005 - [j5]Frank C. Meinecke, Stefan Harmeling, Klaus-Robert Müller:
Inlier-based ICA with an application to superimposed images. Int. J. Imaging Syst. Technol. 15(1): 48-55 (2005) - 2004
- [j4]Erkki Oja, Stefan Harmeling, Luís B. Almeida:
Independent component analysis and beyond. Signal Process. 84(2): 215-216 (2004) - [j3]Stefan Harmeling, Frank C. Meinecke, Klaus-Robert Müller:
Injecting noise for analysing the stability of ICA components. Signal Process. 84(2): 255-266 (2004) - [c3]Frank C. Meinecke, Stefan Harmeling, Klaus-Robert Müller:
Robust ICA for Super-Gaussian Sources. ICA 2004: 217-224 - [c2]Antti Honkela, Stefan Harmeling, Leo Lundqvist, Harri Valpola:
Using Kernel PCA for Initialisation of Variational Bayesian Nonlinear Blind Source Separation Method. ICA 2004: 790-797 - 2003
- [j2]Andreas Ziehe, Motoaki Kawanabe, Stefan Harmeling, Klaus-Robert Müller:
Blind Separation of Post-nonlinear Mixtures using Linearizing Transformations and Temporal Decorrelation. J. Mach. Learn. Res. 4: 1319-1338 (2003) - [j1]Stefan Harmeling, Andreas Ziehe, Motoaki Kawanabe, Klaus-Robert Müller:
Kernel-Based Nonlinear Blind Source Separation. Neural Comput. 15(5): 1089-1124 (2003) - 2001
- [c1]Stefan Harmeling, Andreas Ziehe, Motoaki Kawanabe, Klaus-Robert Müller:
Kernel Feature Spaces and Nonlinear Blind Souce Separation. NIPS 2001: 761-768
Coauthor Index
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