S. Harmeling

发表

How to Explain Individual Classification Decisions pdf

Motoaki Kawanabe, Klaus-Robert Müller, Stefan Harmeling, 2009, J. Mach. Learn. Res..

Learning to detect unseen object classes by between-class attribute transfer

Christoph H. Lampert, Hannes Nickisch, Stefan Harmeling, 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

Gravitational Lensing Accuracy Testing 2010 (GREAT10) Challenge Handbook

M. Bethge, B. Schölkopf, B. Moghaddam, 2010, 1009.0779.

Probabilistic inference for solving (PO) MDPs

Marc Toussaint, Amos Storkey, Stefan Harmeling, 2006 .

Image denoising: Can plain neural networks compete with BM3D?

Stefan Harmeling, Harold Christopher Burger, Christian J. Schuler, 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

Psignifit 4: Pain-free Bayesian Inference for Psychometric Functions.

Stefan Harmeling, Heiko H. Schütt, Jakob Macke, 2015, Journal of vision.

Attribute-Based Classification for Zero-Shot Visual Object Categorization

Christoph H. Lampert, Hannes Nickisch, Stefan Harmeling, 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Greedy Learning of Binary Latent Trees

Christopher K. I. Williams, Stefan Harmeling, S. Harmeling, 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Nonlinear blind source separation using kernel feature spaces

Terrence J. Sejnowski, Lee, Tzyy-Ping Jung, 2001 .

Results of the GREAT08 Challenge?: an image analysis competition for cosmological lensing: Results o

M. Bethge, B. Schölkopf, B. Moghaddam, 2009, 0908.0945.

Image analysis for cosmology: results from the GREAT10 Galaxy Challenge

S. Harmeling, T. Kitching, J. Rhodes, 2012, 1202.5254.

Kernel Feature Spaces and Nonlinear Blind Souce Separation

Motoaki Kawanabe, Andreas Ziehe, Klaus-Robert Müller, 2001, NIPS.

Fast removal of non-uniform camera shake

Bernhard Schölkopf, Michael Hirsch, Stefan Harmeling, 2011, 2011 International Conference on Computer Vision.

Chapter 1 Expectation-Maximization methods for solving ( PO ) MDPs and optimal control problems

Marc Toussaint, A. Storkey, S. Harmeling, 2009 .

Contrastive Representation Learning For Whole Brain Cytoarchitectonic Mapping In Histological Human Brain Sections pdf

Timo Dickscheid, Katrin Amunts, Stefan Harmeling, 2020, 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI).

Separation methods for nonlinear mixtures

J. Karhunen, S. Harmeling, L. Almeida, 2003 .

ArgueBERT: How To Improve BERT Embeddings for Measuring the Similarity of Arguments

S. Harmeling, Maike Behrendt, 2021, KONVENS.

Unsupervised clustering of geophysical data: A critical analysis of traditional climate classifications

Stefan Harmeling, Miguel D. Mahecha, Jakob Zscheischler, 2012 .

A few extreme events dominate global interannual variability in gross primary production

Bernhard Schölkopf, Markus Reichstein, Anja Rammig, 2014 .

Extreme events in gross primary production: a characterization across continents

Markus Reichstein, Anja Rammig, Stefan Harmeling, 2014 .

Convolutional neural networks for cytoarchitectonic brain mapping at large scale pdf

Timo Dickscheid, Alan C. Evans, Katrin Amunts, 2020, NeuroImage.

Deblurring Photographs of Characters Using Deep Neural Networks

S. Harmeling, T. Germer, Tobias Uelwer, 2022, ArXiv.

Learning to detect unseen object classes by between-class attribute transfer

Christoph H. Lampert, Hannes Nickisch, Stefan Harmeling, 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

Using Kernel PCA for Initialisation of Nonlinear Factor Analysis

H. Valpola, A. Honkela, S. Harmeling, 2003 .

Comparison of cardiac volumetry using real-time MRI during free-breathing with standard cine MRI during breath-hold in children

Jan S. Hussmann, S. Harmeling, J. Frahm, 2022, Pediatric Radiology.

Analysing ICA component by injection noise

Noboru Murata, Stefan Harmeling, Frank C. Meinecke, 2003 .

A Closer Look at Embedding Propagation for Manifold Smoothing

F. X. Roca, Pau Rodríguez López, J. M. Gonfaus, 2022, J. Mach. Learn. Res..

Modular Block-diagonal Curvature Approximations for Feedforward Architectures

Philipp Hennig, S. Harmeling, Felix Dangel, 2019, International Conference on Artificial Intelligence and Statistics.

Learning Causal Graphs in Manufacturing Domains using Structural Equation Models

S. Harmeling, Maximilian Kertel, M. Pauly, 2022, 2022 5th International Conference on Artificial Intelligence for Industries (AI4I).

Recording and Playback of Camera Shake: Benchmarking Blind Deconvolution with a Real-World Database

Bernhard Schölkopf, Betty J. Mohler, Michael Hirsch, 2012, ECCV.

Inlier‐based ICA with an application to superimposed images

Klaus-Robert Müller, Stefan Harmeling, Frank C. Meinecke, 2005, Int. J. Imaging Syst. Technol..

Independent component analysis and beyond

Erkki Oja, Luís B. Almeida, Stefan Harmeling, 2004, Signal Process..

Kernel-Based Nonlinear Blind Source Separation

Motoaki Kawanabe, Andreas Ziehe, Klaus-Robert Müller, 2003, Neural Computation.

Technical Report on Separation Methods for Nonlinear Mixtures

Christian Jutten, Stefan Harmeling, Almeida L Karhunen J, 2003 .

From outliers to prototypes: Ordering data

Klaus-Robert Müller, David M. J. Tax, Stefan Harmeling, 2006, Neurocomputing.

How to Test the Quality of Reconstructed Sources in Independent Component Analysis (ICA) of EEG/MEG Data

Bernhard Schölkopf, Thorsten O. Zander, Stefan Harmeling, 2013, 2013 International Workshop on Pattern Recognition in Neuroimaging.

Injecting noise for analysing the stability of ICA components

Klaus-Robert Müller, Stefan Harmeling, Frank C. Meinecke, 2004, Signal Process..

Blind Separation of Post-nonlinear Mixtures using Linearizing Transformations and Temporal Decorrelation

Motoaki Kawanabe, Andreas Ziehe, Klaus-Robert Müller, 2003, J. Mach. Learn. Res..

Mask-Specific Inpainting with Deep Neural Networks

Bernhard Schölkopf, Stefan Harmeling, Christian J. Schuler, 2014, GCPR.

Painfree and accurate Bayesian estimation of psychometric functions for (potentially) overdispersed data

Felix A. Wichmann, Jakob H. Macke, Stefan Harmeling, 2016, Vision Research.

Bayesian Time Series Models: Expectation maximisation methods for solving (PO)MDPs and optimal control problems

Marc Toussaint, Amos Storkey, Stefan Harmeling, 2011 .

Learning to Deblur

Bernhard Schölkopf, Michael Hirsch, Stefan Harmeling, 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

A Machine Learning Approach for Non-blind Image Deconvolution

Bernhard Schölkopf, Stefan Harmeling, Harold Christopher Burger, 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

Image denoising with multi-layer perceptrons, part 1: comparison with existing algorithms and with bounds pdf

Stefan Harmeling, Harold Christopher Burger, Christian J. Schuler, 2012, ArXiv.

Image denoising with multi-layer perceptrons, part 2: training trade-offs and analysis of their mechanisms pdf

Stefan Harmeling, Harold Christopher Burger, Christian J. Schuler, 2012, ArXiv.

Climate Classifications: the Value of Unsupervised Clustering

Stefan Harmeling, Miguel D. Mahecha, Jakob Zscheischler, 2012, ICCS.

Technical report on implementation of linear methods and validation on acoustic sources

Stefan Harmeling, Ziehe A Bünau P, D-T Pham, 2003 .

Space-Variant Single-Image Blind Deconvolution for Removing Camera Shake

Bernhard Schölkopf, Michael Hirsch, Stefan Harmeling, 2010, NIPS.

Transformer-based World Models Are Happy With 100k Interactions

S. Harmeling, Tobias Uelwer, Jan Robine, 2023, ICLR.

Phase Retrieval Using Conditional Generative Adversarial Networks

Stefan Harmeling, Tobias Uelwer, Alexander Oberstrass, 2019, 2020 25th International Conference on Pattern Recognition (ICPR).

Learning How to Combine Internal and External Denoising Methods

Stefan Harmeling, Harold Christopher Burger, Christian J. Schuler, 2013, GCPR.

Efficient filter flow for space-variant multiframe blind deconvolution

Bernhard Schölkopf, Suvrit Sra, Michael Hirsch, 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

Contour Proposal Networks for Biomedical Instance Segmentation

K. Amunts, S. Harmeling, Timo Dickscheid, 2021, Medical Image Anal..

Improving Cytoarchitectonic Segmentation of Human Brain Areas with Self-supervised Siamese Networks pdf

Timo Dickscheid, Katrin Amunts, Stefan Harmeling, 2018, MICCAI.

How Will I Argue? A Dataset for Evaluating Recommender Systems for Argumentations

Stefan Harmeling, Markus Brenneis, Maike Behrendt, 2021, SIGDIAL.

Online Multi-frame Blind Deconvolution with Super-resolution and Saturation Correction

Bernhard Schölkopf, Suvrit Sra, Michael Hirsch, 2011 .

Online blind deconvolution for astronomical imaging

Suvrit Sra, Michael Hirsch, Stefan Harmeling, 2009, 2009 IEEE International Conference on Computational Photography (ICCP).

Detection and attribution of large spatiotemporal extreme events in Earth observation data

Markus Reichstein, Stefan Harmeling, Miguel D. Mahecha, 2013, Ecol. Informatics.

Convolutional neural networks for cytoarchitectonic brain mapping at large scale

Timo Dickscheid, Alan C. Evans, Katrin Amunts, 2020, NeuroImage.

On a Link Between Kernel Mean Maps and Fraunhofer Diffraction, with an Application to Super-Resolution Beyond the Diffraction Limit

Bernhard Schölkopf, Michael Hirsch, Stefan Harmeling, 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

Blind Correction of Optical Aberrations

Bernhard Schölkopf, Michael Hirsch, Stefan Harmeling, 2012, ECCV.

Non-stationary correction of optical aberrations

Bernhard Schölkopf, Michael Hirsch, Stefan Harmeling, 2011, 2011 International Conference on Computer Vision.

How Much Do I Argue Like You? Towards a Metric on Weighted Argumentation Graphs

Martin Mauve, Stefan Harmeling, Markus Brenneis, 2020, SAFA@COMMA.

Multiframe blind deconvolution, super-resolution, and saturation correction via incremental EM

Bernhard Schölkopf, Suvrit Sra, Michael Hirsch, 2010, 2010 IEEE International Conference on Image Processing.

Blindly Deconvolving Super-noisy Blurry Image Sequences

S. Harmeling, Leonid Kostrykin, 2022, ArXiv.

Optimizing Intermediate Representations of Generative Models for Phase Retrieval

S. Harmeling, S. Konietzny, Tobias Uelwer, 2022, Trans. Mach. Learn. Res..

Non-Iterative Phase Retrieval With Cascaded Neural Networks

Stefan Harmeling, Tobias Uelwer, Tobias Hoffmann, 2021, ICANN.

Bayesian Estimators for Robins-Ritovs Problem

S Harmeling, S. Harmeling, 2007 .

Improving alpha matting and motion blurred foreground estimation

Bernhard Schölkopf, Michael Hirsch, Stefan Harmeling, 2013, 2013 IEEE International Conference on Image Processing.

PyMatting: A Python Library for Alpha Matting

S. Harmeling, T. Germer, Tobias Uelwer, 2020, J. Open Source Softw..

Uncertainty-Aware Contour Proposal Networks for Cell Segmentation in Multi-Modality High-Resolution Microscopy Images

K. Amunts, S. Harmeling, Timo Dickscheid, 2022, Cell Segmentation Challenge @ NeurIPS.

A Survey on Self-Supervised Representation Learning

S. Harmeling, S. Konietzny, Maike Behrendt, 2023, ArXiv.

Spirometry‐based reconstruction of real‐time cardiac MRI: Motion control and quantification of heart–lung interactions

Jan S. Hussmann, S. Harmeling, J. Frahm, 2021, Magnetic resonance in medicine.

Inferring textual entailment with a probabilistically sound calculus*

Stefan Harmeling, S. Harmeling, 2009, Natural Language Engineering.

Learning to Plan via a Multi-Step Policy Regression Method

Stefan Wagner, Stefan Harmeling, Tobias Uelwer, 2021, ICANN.

Limited-Angle Tomography Reconstruction via Deep End-To-End Learning on Synthetic Data

S. Harmeling, S. Konietzny, Tobias Uelwer, 2023, Applied Mathematics for Modern Challenges.

A Closer Look at Reference Learning for Fourier Phase Retrieval pdf

Stefan Harmeling, Tobias Uelwer, Nick Rucks, 2021, ArXiv.

Contrastive Representation Learning For Whole Brain Cytoarchitectonic Mapping In Histological Human Brain Sections

K. Amunts, S. Harmeling, C. Schiffer, 2020, 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI).

Smaller World Models for Reinforcement Learning

S. Harmeling, Tobias Uelwer, Jan Robine, 2023, Neural Processing Letters.

Solving Satisfiability Problems with Genetic Algorithms

Stefan Harmeling, J. R. Koza, J. Koza, 2000 .

Learning Conditional Generative Models for Phase Retrieval

S. Harmeling, S. Konietzny, Tobias Uelwer, 2022 .

Attribute-Based Classification for Zero-Shot Visual Object Categorization

Christoph H. Lampert, H. Nickisch, S. Harmeling, 2022 .

Fast Multi-Level Foreground Estimation pdf

Stefan Harmeling, Tobias Uelwer, Thomas Germer, 2020, 2020 25th International Conference on Pattern Recognition (ICPR).

De Novo Prediction of RNA 3D Structures with Deep Learning

S. Harmeling, M. Kollmann, Julius Ramakers, 2021, bioRxiv.

Computing functions of random variables via reproducing kernel Hilbert space representations

Bernhard Schölkopf, Kenji Fukumizu, Krikamol Muandet, 2015, Statistics and Computing.