Joachim M. Buhmann

发表

Prostate MRI Segmentation Using Learned Semantic Knowledge and Graph Cuts

Joachim M. Buhmann, Dwarikanath Mahapatra, J. Buhmann, 2014, IEEE Transactions on Biomedical Engineering.

SuperSlicing Frame Restoration for Anisotropic ssTEM and Video Data

Joachim M. Buhmann, Dmitry Laptev, 2014, Neural Connectomics.

The Minimum Transfer Cost Principle for Model-Order Selection

Joachim M. Buhmann, Mario Frank, Morteza Haghir Chehreghani, 2011, ECML/PKDD.

A New Distance Measure for Probabilistic Shape Modeling

Joachim M. Buhmann, Wei-Jun Chen, 2003, DAGM-Symposium.

SPINDLE: End-to-end learning from EEG/EMG to extrapolate animal sleep scoring across experimental settings, labs and species

Joachim M. Buhmann, Ðorðe Miladinovic, Christine Muheim, 2019, PLoS Comput. Biol..

Proteome coverage prediction with infinite Markov models

Joachim M. Buhmann, Manfred Claassen, Ruedi Aebersold, 2009, Bioinform..

Visualizing Group Structure

Joachim M. Buhmann, Marcus Held, Jan Puzicha, 1998, NIPS.

Mobil robot Rhino

Wolfram Burgard, Joachim M. Buhmann, Dieter Fox, 1995 .

Model Selection in Kernel Methods Based on a Spectral Analysis of Label Information

Joachim M. Buhmann, Mikio L. Braun, Tilman Lange, 2006, DAGM-Symposium.

Complex Statistical Models for Object Recognition and Mass Spectrometry

Joachim M. Buhmann, 2007, PRIS.

Agnostic Domain Adaptation

Joachim M. Buhmann, Alexander Vezhnevets, 2011, DAGM-Symposium.

Supplementary Information: Ellipsoidal Multiple Instance Learning

Joachim M. Buhmann, Cheng Soon Ong, Gabriel Krummenacher, 2002 .

Illumination-Invariant Face Recognition with a Contrast Sensitive Silicon Retina

Joachim M. Buhmann, Frank H. Eeckman, Martin Lades, 1993, NIPS.

Multidimensional Scaling and Data Clustering

Joachim M. Buhmann, Thomas Hofmann, 1994, NIPS.

PepSplice: cache-efficient search algorithms for comprehensive identification of tandem mass spectra

Joachim M. Buhmann, Peter Widmayer, Bernd Fischer, 2007, Bioinform..

The Balanced Accuracy and Its Posterior Distribution

Joachim M. Buhmann, Cheng Soon Ong, Klaas E. Stephan, 2010, 2010 20th International Conference on Pattern Recognition.

Vector quantization with complexity costs

Joachim M. Buhmann, Hans Kühnel, J. Buhmann, 1993, IEEE Trans. Inf. Theory.

Cluster analysis of heterogeneous rank data

Joachim M. Buhmann, Ludwig M. Busse, Peter Orbanz, 2007, ICML '07.

Structure Identification by Optimized Interventions

Joachim M. Buhmann, Alberto Giovanni Busetto, 2009, AISTATS.

Region-based motion compensated 3D-wavelet transform coding of video

Joachim M. Buhmann, Andreas Polzer, Hansjörg Klock, 1997, Proceedings of International Conference on Image Processing.

Clustering with the Connectivity Kernel

Joachim M. Buhmann, Volker Roth, Bernd Fischer, 2003, NIPS.

Robust optimization in the presence of uncertainty: A generic approach

Joachim M. Buhmann, Matús Mihalák, Peter Widmayer, 2018, J. Comput. Syst. Sci..

Multiscale Annealing for Grouping and Unsupervised Texture Segmentation

Joachim M. Buhmann, Jan Puzicha, J. Buhmann, 1999, Comput. Vis. Image Underst..

Probabilistic image registration and anomaly detection by nonlinear warping

Joachim M. Buhmann, Bernd Fischer, Verena Kaynig, 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

Computing with Arrays of Coupled Oscillators

Joachim M. Buhmann, Pierre Baldi, Ron Meir, 1990 .

Competitive learning algorithms for robust vector quantization

Joachim M. Buhmann, Thomas Hofmann, J. Buhmann, 1998, IEEE Trans. Signal Process..

Superslicing frame restoration for anisotropic sstem

Joachim M. Buhmann, Dmitry Laptev, A. Veznevets, 2014, 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI).

Fusion of Similarity Data in Clustering

Joachim M. Buhmann, Tilman Lange, J. Buhmann, 2005, NIPS.

Kernel Expansion for Online Preference Tracking

Joachim M. Buhmann, Yvonne Moh, J. Buhmann, 2008, ISMIR.

Selecting the rank of SVD by Maximum Approximation Capacity pdf

Joachim M. Buhmann, Mario Frank, 2011, ArXiv.

Speech Enhancement Using Generative Dictionary Learning

Joachim M. Buhmann, Christian D. Sigg, Tomas Dikk, 2012, IEEE Transactions on Audio, Speech, and Language Processing.

Unsupervised Texture Segmentation in a Deterministic Annealing Framework

Joachim M. Buhmann, Thomas Hofmann, Jan Puzicha, 1998, IEEE Trans. Pattern Anal. Mach. Intell..

Stability-Based Validation of Clustering Solutions

Joachim M. Buhmann, Volker Roth, Mikio L. Braun, 2004, Neural Computation.

Regularized online learning of pseudometrics

Joachim M. Buhmann, Yvonne Moh, 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

Regularized Data Fusion Improves Image Segmentation

Joachim M. Buhmann, Tilman Lange, J. Buhmann, 2007, DAGM-Symposium.

Classification of brain MRI with big data and deep 3D convolutional neural networks

Joachim M. Buhmann, Viktor Wegmayr, Sai Aitharaju, 2018, Medical Imaging.

Dithered Color Quantization

Joachim M. Buhmann, Dieter W. Fellner, Marcus Held, 1998, Comput. Graph. Forum.

Nonparametric Bayesian Image Segmentation

Joachim M. Buhmann, Peter Orbanz, J. Buhmann, 2008, International Journal of Computer Vision.

Knowledge-Discovery: Wie können wir Muster und Strukturen in Daten zuverlässig erkennen?

Joachim M. Buhmann, Joachim M. Buhmann, 1998, Künstliche Intell..

Weakly supervised semantic segmentation with a multi-image model

Joachim M. Buhmann, Vittorio Ferrari, Alexander Vezhnevets, 2011, 2011 International Conference on Computer Vision.

Combined color and texture segmentation by parametric distributional clustering

Joachim M. Buhmann, Thomas Zöller, Lothar Hermes, 2002, Object recognition supported by user interaction for service robots.

Data-driven fiber tractography with neural networks

Joachim M. Buhmann, Viktor Wegmayr, Giacomo Giuliari, 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

Learning Counterfactual Representations for Estimating Individual Dose-Response Curves

Stefan Bauer, Joachim M. Buhmann, Walter Karlen, 2019, AAAI.

Entrack: Probabilistic Spherical Regression with Entropy Regularization for Fiber Tractography

Joachim M. Buhmann, Viktor Wegmayr, J. Buhmann, 2020, International Journal of Computer Vision.

On the definition of role mining

Joachim M. Buhmann, David A. Basin, Mario Frank, 2010, SACMAT '10.

Joint segmentation and groupwise registration of cardiac DCE MRI using sparse data representations

Joachim M. Buhmann, Zhang Li, Frans Vos, 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).

Learning the Compositional Nature of Visual Objects

Joachim M. Buhmann, Björn Ommer, J. Buhmann, 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

Self-organized Clustering of Mixture Models for Combined Color And Texture Segmentation

Joachim M. Buhmann, Thomas Zöller, J. Buhmann, 2002 .

On learning texture edge detectors

Joachim M. Buhmann, Jan Puzicha, S. Will, 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

Greedy MaxCut algorithms and their information content

Joachim M. Buhmann, Alexey Gronskiy, Yatao Bian, 2015, ITW 2015.

Optimized expected information gain for nonlinear dynamical systems

Joachim M. Buhmann, Cheng Soon Ong, Alberto Giovanni Busetto, 2009, ICML '09.

Optimization in the presence of uncertainty

Joachim M. Buhmann, Peter Widmayer, MatÃºÅ¡ MihalÃ¡k, 2012 .

Unsupervised On-line Learning of Decision Trees for Hierarchical Data Analysis

Joachim M. Buhmann, Marcus Held, 1997, NIPS.

Pipeline validation for connectivity-based cortex parcellation

Nico S. Gorbach, Joachim M. Buhmann, Marc Tittgemeyer, 2018, NeuroImage.

Coupled Clustering: A Method for Detecting Structural Correspondence

Joachim M. Buhmann, Ido Dagan, Eli Shamir, 2001, J. Mach. Learn. Res..

Computational Design of Informative Experiments in Systems Biology

Joachim M. Buhmann, Mikael Sunnåker, Alberto Giovanni Busetto, 2014 .

Density Estimation of Initial Conditions for Populations of Dynamical Systems PASCAL 2008 Workshop on Approximate Inference in Stochastic Processes and Dynamical Systems

Joachim M. Buhmann, Alberto Giovanni Busetto, Bernd Fischer, 2008 .

SIMBAD: Emergence of Pattern Similarity

Joachim M. Buhmann, 2013, Similarity-Based Pattern Analysis and Recognition.

Compositional Object Recognition, Segmentation, and Tracking in Video

Joachim M. Buhmann, Björn Ommer, J. Buhmann, 2007, EMMCVPR.

Learning the Compositional Nature of Visual Object Categories for Recognition

Joachim M. Buhmann, Björn Ommer, J. Buhmann, 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Active learning for semantic segmentation with expected change

Joachim M. Buhmann, Vittorio Ferrari, Alexander Vezhnevets, 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

TI-POOLING: Transformation-Invariant Pooling for Feature Learning in Convolutional Neural Networks

Joachim M. Buhmann, Marc Pollefeys, Dmitry Laptev, 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Stable Bayesian Parameter Estimation for Biological Dynamical Systems

Joachim M. Buhmann, Alberto Giovanni Busetto, J. Buhmann, 2009, 2009 International Conference on Computational Science and Engineering.

Crohn's disease tissue segmentation from abdominal MRI using semantic information and graph cuts

Joachim M. Buhmann, Frans Vos, Dwarikanath Mahapatra, 2013, 2013 IEEE 10th International Symposium on Biomedical Imaging.

Histogram clustering for unsupervised segmentation and image retrieval

Joachim M. Buhmann, Thomas Hofmann, Jan Puzicha, 1999, Pattern Recognit. Lett..

Generative Aging Of Brain MRI For Early Prediction Of MCI-AD Conversion

Joachim M. Buhmann, Viktor Wegmayr, Maurice Hörold, 2019, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019).

Visual Saliency Based Active Learning for Prostate MRI Segmentation

Joachim M. Buhmann, Dwarikanath Mahapatra, J. Buhmann, 2015, MLMI.

Multiscale Annealing and Robustness: Fast Heuristics for Large Scale Non-linear Optimization

Joachim M. Buhmann, Jan Puzicha, J. Buhmann, 2001 .

Obtaining Consensus Annotations For Retinal Image Segmentation Using Random Forest And Graph Cuts

Joachim M. Buhmann, Dwarikanath Mahapatra, J. Buhmann, 2015 .

Crohn's disease segmentation from MRI using learned image priors

Joachim M. Buhmann, Frans Vos, Dwarikanath Mahapatra, 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).

Stability-Based Model Selection

Joachim M. Buhmann, Volker Roth, Mikio L. Braun, 2002, NIPS.

Learning with constrained and unlabelled data

Joachim M. Buhmann, Anil K. Jain, Tilman Lange, 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

A generative model of whole-brain effective connectivity

Joachim M. Buhmann, Klaas E. Stephan, Lars Kasper, 2018, NeuroImage.

Data Resampling for Path Based Clustering

Joachim M. Buhmann, Bernd Fischer, J. Buhmann, 2002, DAGM-Symposium.

Automated Analysis of Tissue Micro-Array Images on the Example of Renal Cell Carcinoma

Joachim M. Buhmann, Volker Roth, Cheng Soon Ong, 2013, Similarity-Based Pattern Analysis and Recognition.

Disentangled State Space Models: Unsupervised Learning of dynamics across Heterogeneous Environments

Stefan Bauer, Joachim M. Buhmann, Bernhard Schölkopf, 2019, DGS@ICLR.

Weakly Supervised Cell Nuclei Detection and Segmentation on Tissue Microarrays of Renal Clear Cell Carcinoma

Joachim M. Buhmann, Peter J. Wild, Thomas J. Fuchs, 2008, DAGM-Symposium.

Complexity optimized vector quantization: a neural network approach

Joachim M. Buhmann, Hans Kühnel, 1992, Data Compression Conference, 1992..

Unsupervised and supervised data clustering with competitive neural networks

Joachim M. Buhmann, H. Kuhnel, J. Buhmann, 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.

Computational modeling for assessment of IBD: To be or not to be?

Joachim M. Buhmann, Zhang Li, Frans Vos, 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

Grosser Systeme Echtzeitoptimierung Schwerpunktprogramm Der Deutschen Forschungsgemeinschaft Empirical Risk Approximation: an Induction Principle for Unsupervised Learning

Joachim M. Buhmann, J. Buhmann, 2022 .

Proteome Coverage Prediction for Integrated Proteomics Datasets

Joachim M. Buhmann, Manfred Claassen, Ruedi Aebersold, 2011, J. Comput. Biol..

Automatic Detection and Segmentation of Crohn's Disease Tissues From Abdominal MRI

Joachim M. Buhmann, Frans Vos, Dwarikanath Mahapatra, 2013, IEEE Transactions on Medical Imaging.

A class of probabilistic models for role engineering

Joachim M. Buhmann, David A. Basin, Mario Frank, 2008, CCS.

Joachim M. Buhmann, Brian McWilliams, David Balduzzi, 2013, NIPS.

Stochastic Algorithms for Exploratory Data Analysis: Data Clustering and Data Visualization

Joachim M. Buhmann, J. Buhmann, 1998, Learning in Graphical Models.

Multi-assignment clustering for Boolean data

Joachim M. Buhmann, David A. Basin, Mario Frank, 2009, ICML '09.

Geometrical Consistent 3D Tracing of Neuronal Processes in ssTEM Data

Joachim M. Buhmann, Thomas J. Fuchs, Verena Kaynig, 2010, MICCAI.

Scalable Adaptive Stochastic Optimization Using Random Projections

Joachim M. Buhmann, Brian McWilliams, Gabriel Krummenacher, 2016, NIPS.

Correction to "Pairwise Data Clustering by

Joachim M. Buhmann, Thomas Hofmann, 1997 .

Bayesian mixed-effects inference on classification performance in hierarchical data sets

Joachim M. Buhmann, Cheng Soon Ong, Klaas E. Stephan, 2012, J. Mach. Learn. Res..

Fast and Robust Least Squares Estimation in Corrupted Linear Models

Joachim M. Buhmann, Brian McWilliams, Gabriel Krummenacher, 2014, NIPS.

A field of experts model for optic cup and disc segmentation from retinal fundus images

Joachim M. Buhmann, Dwarikanath Mahapatra, 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).

Sparse feature selection by information theory

Joachim M. Buhmann, Stuart Geman, Guangyao Zhou, 2014, 2014 IEEE International Symposium on Information Theory.

Central and Pairwise Data Clustering by Competitive Neural Networks

Joachim M. Buhmann, Thomas Hofmann, 1993, NIPS.

Breakout Session Report: Learning and Perceptual Organization

Joachim M. Buhmann, Eric Saund, Daniel Crevier, 2000 .

A maximum entropy approach to pairwise data clustering

Joachim M. Buhmann, Thomas Hofmann, 1994, Proceedings of the 12th IAPR International Conference on Pattern Recognition, Vol. 3 - Conference C: Signal Processing (Cat. No.94CH3440-5).

A Deterministic Annealing Framework for Unsupervised Texture Segmentation

Joachim M. Buhmann, Thomas Hofmann, Jan Puzicha, 1996 .

Komplexe lernende Systeme: der mobile Roboter RHINO

Joachim M. Buhmann, Armin B. Cremers, Sebastian Thrun, 1995, Künstliche Intell..

Kickback Cuts Backprop's Red-Tape: Biologically Plausible Credit Assignment in Neural Networks pdf

Joachim M. Buhmann, David Balduzzi, Hastagiri Vanchinathan, 2014, AAAI.

Transformation-Invariant Convolutional Jungles

Joachim M. Buhmann, Dmitry Laptev, J. Buhmann, 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

A minimum entropy approach to adaptive image polygonization

Joachim M. Buhmann, Lothar Hermes, J. Buhmann, 2003, IEEE Trans. Image Process..

Generative Embedding for Model-Based Classification of fMRI Data

Joachim M. Buhmann, Cheng Soon Ong, Klaas E. Stephan, 2011, PLoS Comput. Biol..

Semi-supervised LC/MS alignment for differential proteomics

Joachim M. Buhmann, Volker Roth, Bernd Fischer, 2006, ISMB.

Continuous DR-submodular Maximization: Structure and Algorithms

Andreas Krause, Joachim M. Buhmann, Kfir Y. Levy, 2017, NIPS 2017.

The information content in sorting algorithms

Joachim M. Buhmann, Morteza Haghir Chehreghani, Ludwig M. Busse, 2012, 2012 IEEE International Symposium on Information Theory Proceedings.

Convolutional Decision Trees for Feature Learning and Segmentation

Joachim M. Buhmann, Dmitry Laptev, J. Buhmann, 2014, GCPR.

Spanning Tree Approximations for Conditional Random Fields

Joachim M. Buhmann, Cheng Soon Ong, Patrick Pletscher, 2009, AISTATS.

Unsupervised Learning without Overfitting: Empirical Risk Approximation as an Induction Principle for Reliable Clustering

Joachim M. Buhmann, Marcus Held, J. Buhmann, 1999 .

Model Selection for Gaussian Process Regression

Stefan Bauer, Nico S. Gorbach, Joachim M. Buhmann, 2017, GCPR.

Active learning based segmentation of Crohns disease from abdominal MRI

Joachim M. Buhmann, Frans Vos, Dwarikanath Mahapatra, 2016, Comput. Methods Programs Biomed..

On Relevant Dimensions in Kernel Feature Spaces

Joachim M. Buhmann, Klaus-Robert Müller, Mikio L. Braun, 2008, J. Mach. Learn. Res..

Smooth Image Segmentation by Nonparametric Bayesian Inference

Joachim M. Buhmann, Peter Orbanz, J. Buhmann, 2006, ECCV.

Bayesian Order-Adaptive Clustering for Video Segmentation

Joachim M. Buhmann, Peter Orbanz, Samuel Braendle, 2007, EMMCVPR.

Classification of Multi-labeled Data: A Generative Approach

Joachim M. Buhmann, Andreas P. Streich, J. Buhmann, 2008, ECML/PKDD.

Object recognition with Gabor functions in the dynamic link architecture

Joachim M. Buhmann, Christoph von der Malsburg, Jan C. Vorbrüggen, 1992 .

Oscillatory associative memories

Joachim M. Buhmann, 1998 .

Anisotropic ssTEM Image Segmentation Using Dense Correspondence across Sections

Joachim M. Buhmann, Dmitry Laptev, Alexander Vezhnevets, 2012, MICCAI.

Inter-active learning of randomized tree ensembles for object detection

Joachim M. Buhmann, Thomas J. Fuchs, 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

Exact Recovery for a Family of Community-Detection Generative Models

Joachim M. Buhmann, Paolo Penna, Luca Mondada, 2019, 2019 IEEE International Symposium on Information Theory (ISIT).

A model-based approach to predicting graduate-level performance using indicators of undergraduate-level performance

Joachim M. Buhmann, Hans Rudolf Heinimann, Kay H. Brodersen, 2015, EDM 2015.

VIS Capstone Address : Can I believe what I see?-Information theoretic algorithm validation

Joachim M. Buhmann, 2018, VAST.

Support vector machines for land usage classification in Landsat TM imagery

Joachim M. Buhmann, Jan Puzicha, L. Hermes, 1999, IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No.99CH36293).

Model Selection in Clustering by Uniform Convergence Bounds

Joachim M. Buhmann, Marcus Held, 1999, NIPS.

A Compositionally Architecture for Perceptual Feature Grouping

Joachim M. Buhmann, Björn Ommer, J. Buhmann, 2003, EMMCVPR.

Model-Based Clustering of Inhomogeneous Paired Comparison Data

Joachim M. Buhmann, Ludwig M. Busse, J. Buhmann, 2011, SIMBAD.

Scalable Variational Inference for Dynamical Systems

Stefan Bauer, Nico S. Gorbach, Joachim M. Buhmann, 2017, NIPS.

A New Contour-Based Approach to Object Recognition for Assembly Line Robots

Joachim M. Buhmann, Lothar Hermes, Markus Suing, 2001, DAGM-Symposium.

Data visualization by multidimensional scaling: a deterministic annealing approach

Joachim M. Buhmann, Hansjörg Klock, J. Buhmann, 1996, Pattern Recognit..

Generative Aging of Brain MR-Images and Prediction of Alzheimer Progression

Joachim M. Buhmann, Viktor Wegmayr, Maurice Hörold, 2019, GCPR.

Learning Dictionaries With Bounded Self-Coherence

Joachim M. Buhmann, Christian D. Sigg, Tomas Dikk, 2012, IEEE Signal Processing Letters.

Role Mining with Probabilistic Models

Joachim M. Buhmann, David A. Basin, Mario Frank, 2013, TSEC.

Approximate Sorting

Joachim M. Buhmann, Morteza Haghir Chehreghani, Ludwig M. Busse, 2013, GCPR.

Efficient and Flexible Inference for Stochastic Systems

Stefan Bauer, Nico S. Gorbach, Joachim M. Buhmann, 2017, NIPS.

Probabilistic De Novo Peptide Sequencing with Doubly Charged Ions

Joachim M. Buhmann, Bernd Fischer, Hansruedi Peter, 2006, DAGM-Symposium.

Optimal Cluster Preserving Embedding of Nonmetric Proximity Data

Joachim M. Buhmann, Motoaki Kawanabe, Volker Roth, 2003, IEEE Trans. Pattern Anal. Mach. Intell..

Single-class Support Vector Machines

Joachim M. Buhmann, Bernhard Schölkopf, John Shawe-Taylor, 1999 .

Semi-supervised Image Segmentation by Parametric Distributional Clustering

Joachim M. Buhmann, Lothar Hermes, 2003, EMMCVPR.

Contextual classification by entropy-based polygonization

Joachim M. Buhmann, Lothar Hermes, J. Buhmann, 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

Crowdsourcing the creation of image segmentation algorithms for connectomics

Joachim M. Buhmann, Changming Sun, H. Sebastian Seung, 2015, Front. Neuroanat..

Automatic cardiac RV segmentation using semantic information with graph cuts

Joachim M. Buhmann, Dwarikanath Mahapatra, J. Buhmann, 2013, 2013 IEEE 10th International Symposium on Biomedical Imaging.

Dense Stereo by Triangular Meshing and Cross Validation

Joachim M. Buhmann, Bernd Fischer, Herbert Bay, 2006, DAGM-Symposium.

Neuron geometry extraction by perceptual grouping in ssTEM images

Joachim M. Buhmann, Thomas J. Fuchs, Verena Kaynig, 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

Unsupervised Mitral Valve Segmentation in Echocardiography with Neural Network Matrix Factorization

Joachim M. Buhmann, Francesco Maisano, Luca Corinzia, 2019, AIME.

Object Categorization by Compositional Graphical Models

Joachim M. Buhmann, Björn Ommer, 2005, EMMCVPR.

Manifold regularization for semi-supervised sequential learning

Joachim M. Buhmann, Yvonne Moh, J. Buhmann, 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

Computational TMA Analysis and Cell Nucleus Classification of Renal Cell Carcinoma

Joachim M. Buhmann, Volker Roth, Cheng Soon Ong, 2010, DAGM-Symposium.

Deterministic Annealing for Unsupervised Texture Segmentation

Joachim M. Buhmann, Thomas Hofmann, Jan Puzicha, 1997, EMMCVPR.

Combining Multiple Expert Annotations Using Semi-supervised Learning and Graph Cuts for Crohn's Disease Segmentation

Joachim M. Buhmann, Frans Vos, Dwarikanath Mahapatra, 2014, ABDI@MICCAI.

Graph-Based Pancreatic Islet Segmentation for Early Type 2 Diabetes Mellitus on Histopathological Tissue

Joachim M. Buhmann, Thomas J. Fuchs, Holger Moch, 2009, MICCAI.

Time-series alignment by non-negative multiple generalized canonical correlation analysis

Joachim M. Buhmann, Volker Roth, Bernd Fischer, 2007, BMC Bioinformatics.

Robust optimization in the presence of uncertainty

Joachim M. Buhmann, Matús Mihalák, Peter Widmayer, 2013, ITCS '13.

Semi-Supervised and Active Learning for Automatic Segmentation of Crohn's Disease

Joachim M. Buhmann, Frans Vos, Dwarikanath Mahapatra, 2013, MICCAI.

Music preference learning with partial information

Joachim M. Buhmann, Yvonne Moh, Peter Orbanz, 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

A Silicon Retina for Face Recognition

Joachim M. Buhmann, Martin Lades, Frank H. Eeckman, 1993 .

Pairwise Data Clustering by Deterministic Annealing

Joachim M. Buhmann, Thomas Hofmann, J. Buhmann, 1997, IEEE Trans. Pattern Anal. Mach. Intell..

Combining partitions by probabilistic label aggregation

Joachim M. Buhmann, Tilman Lange, J. Buhmann, 2005, KDD '05.

Automatic Detection of Learnability under Unreliable and Sparse User Feedback

Joachim M. Buhmann, Wolfgang Einhäuser, Yvonne Moh, 2008, DAGM-Symposium.

The Mobile Robot Rhino

Wolfram Burgard, Joachim M. Buhmann, Armin B. Cremers, 1995, SNN Symposium on Neural Networks.

Asymptotic analysis of estimators on multi-label data

Joachim M. Buhmann, Andreas P. Streich, 2014, Machine Learning.

The Mobile Robot RHINO

Wolfram Burgard, Joachim M. Buhmann, Dieter Fox, 1995, AI Mag..

TMARKER: A free software toolkit for histopathological cell counting and staining estimation

Joachim M. Buhmann, Cheng Soon Ong, Peter J. Wild, 2013, Journal of pathology informatics.

A Resampling Approach to Cluster Validation

Joachim M. Buhmann, Volker Roth, Mikio L. Braun, 2002, COMPSTAT.

Real-time phase-based stereo for a mobile robot

Joachim M. Buhmann, T. Frohlinghaus, J. Buhmann, 1996, Proceedings of the First Euromicro Workshop on Advanced Mobile Robots (EUROBOT '96).

Statistical and computational thresholds for the planted k-densest sub-hypergraph problem pdf

Joachim M. Buhmann, Wojciech Szpankowski, Paolo Penna, 2020, ArXiv.

Non-parametric similarity measures for unsupervised texture segmentation and image retrieval

Joachim M. Buhmann, Thomas Hofmann, Jan Puzicha, 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

Proteome Coverage Prediction for Integrated Proteomics Datasets

Joachim M. Buhmann, Manfred Claassen, Ruedi Aebersold, 2010, RECOMB.

Clustering in Computer Vision and Data Analysis

Joachim M. Buhmann, J. Buhmann, 2004, PRIS.

Discrete Mixture Models for Unsupervised Image Segmentation

Joachim M. Buhmann, Thomas Hofmann, Jan Puzicha, 1998, DAGM-Symposium.

Boosting Convolutional Filters with Entropy Sampling for Optic Cup and Disc Image Segmentation from Fundus Images

Joachim M. Buhmann, Dwarikanath Mahapatra, Julian G. Zilly, 2015, MLMI.

Glaucoma detection using entropy sampling and ensemble learning for automatic optic cup and disc segmentation

Joachim M. Buhmann, Dwarikanath Mahapatra, Julian G. Zilly, 2017, Comput. Medical Imaging Graph..

Complexity Optimized Data Clustering by Competitive Neural Networks

Joachim M. Buhmann, Hans Kühnel, J. Buhmann, 1993, Neural Computation.

Time-Series Alignment by Non-negative Multiple Generalized Canonical Correlation Analysis

Joachim M. Buhmann, Volker Roth, Bernd Fischer, 2007, WILF.

Multi-Organ Cancer Classification and Survival Analysis pdf

Stefan Bauer, Joachim M. Buhmann, Peter J. Wild, 2016, ArXiv.

Robust Image Segmentation Using Resampling and Shape Constraints

Joachim M. Buhmann, Thomas Zöller, J. Buhmann, 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Model-based feature construction for multivariate decoding

Joachim M. Buhmann, Cheng Soon Ong, Klaas E. Stephan, 2011, NeuroImage.

Topology free hidden Markov models: application to background modeling

Joachim M. Buhmann, Nikos Paragios, Visvanathan Ramesh, 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

Decoding the perception of pain from fMRI using multivariate pattern analysis

Joachim M. Buhmann, Irene Tracey, Klaas E. Stephan, 2012, NeuroImage.

Weakly supervised semantic segmentation of Crohn's disease tissues from abdominal MRI

Joachim M. Buhmann, Frans Vos, Dwarikanath Mahapatra, 2013, 2013 IEEE 10th International Symposium on Biomedical Imaging.

Optimal Continuous DR-Submodular Maximization and Applications to Provable Mean Field Inference

Andreas Krause, Joachim M. Buhmann, Yatao A. Bian, 2019, ICML.

Learning Compositional Categorization Models

Joachim M. Buhmann, Björn Ommer, J. Buhmann, 2006, ECCV.

Computational Pathology: Challenges and Promises for Tissue Analysis

Joachim M. Buhmann, Thomas J. Fuchs, J. Buhmann, 2015, Comput. Medical Imaging Graph..

Pattern Segmentation in Associative Memory

Joachim M. Buhmann, DeLiang Wang, Christoph von der Malsburg, 1990, Neural Computation.

Bagging for Path-Based Clustering

Joachim M. Buhmann, Bernd Fischer, J. Buhmann, 2003, IEEE Trans. Pattern Anal. Mach. Intell..

Neural collaborative filtering for unsupervised mitral valve segmentation in echocardiography

Joachim M. Buhmann, Maurizio Taramasso, Francesco Maisano, 2020, Artif. Intell. Medicine.

Variational Bayesian mixed-effects inference for classification studies

Joachim M. Buhmann, Klaas E. Stephan, Jean Daunizeau, 2013, NeuroImage.

Kernel-Based Grouping of Histogram Data

Joachim M. Buhmann, Tilman Lange, 2007, ECML.

On the information and representation of non-Euclidean pairwise data

Joachim M. Buhmann, Volker Roth, Klaus-Robert Müller, 2006, Pattern Recognit..

Model Order Selection and Cue Combination for Image Segmentation

Joachim M. Buhmann, Serge J. Belongie, Tilman Lange, 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

A Model Development Pipeline for Crohn's Disease Severity Assessment from Magnetic Resonance Images

Joachim M. Buhmann, Frans Vos, Dwarikanath Mahapatra, 2013, Abdominal Imaging.

Free Energy Asymptotics for Problems with Weak Solution Dependencies

Joachim M. Buhmann, Wojciech Szpankowski, Alexey Gronskiy, 2018, 2018 IEEE International Symposium on Information Theory (ISIT).

On maximum-likelihood estimation in the all-or-nothing regime

Joachim M. Buhmann, Wojciech Szpankowski, Paolo Penna, 2021, 2021 IEEE International Symposium on Information Theory (ISIT).

Path-Based Clustering for Grouping of Smooth Curves and Texture Segmentation

Joachim M. Buhmann, Bernd Fischer, J. Buhmann, 2003, IEEE Trans. Pattern Anal. Mach. Intell..

Instance Segmentation for the Quantification of Microplastic Fiber Images

Joachim M. Buhmann, Aytunc Sahin, Viktor Wegmayr, 2020, 2020 IEEE Winter Conference on Applications of Computer Vision (WACV).

Introduction to the special issue on neural networks for data mining and knowledge discovery

Joachim M. Buhmann, Jacek M. Zurada, Yoshua Bengio, 2000 .

Context Sensitive Information: Model Validation by Information Theory

Joachim M. Buhmann, 2012, ICPRAM.

Towards a Framework for Modelling Engagement Dynamics in Multiple Learning Domains

Joachim M. Buhmann, Markus H. Gross, Barbara Solenthaler, 2013, Int. J. Artif. Intell. Educ..

Greedy MaxCut algorithms and their information content

Joachim M. Buhmann, Alexey Gronskiy, Yatao Bian, 2015, 2015 IEEE Information Theory Workshop (ITW).

Image Segmentation by Networks of Spiking Neurons

Joachim M. Buhmann, Tilman Lange, Ulrich Ramacher, 2005, Neural Computation.

Structure Preserving Embedding of Dissimilarity Data

Joachim M. Buhmann, Volker Roth, Thomas J. Fuchs, 2013, Similarity-Based Pattern Analysis and Recognition.

Multiscale annealing for real-time unsupervised texture segmentation

Joachim M. Buhmann, Jan Puzicha, J. Buhmann, 1997, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

An Annealed "Neural Gas" Network for Robust Vector Quantization

Joachim M. Buhmann, Thomas Hofmann, 1996, ICANN.

Guarantees for Greedy Maximization of Non-submodular Functions with Applications pdf

Andreas Krause, Joachim M. Buhmann, Sebastian Tschiatschek, 2017, ICML.

2010 International Conference on Pattern Recognition The binormal assumption on precision-recall curves

Joachim M. Buhmann, Cheng Soon Ong, Klaas E. Stephan, 2022 .

Empirical Evaluation of Dissimilarity Measures for Color and Texture

Joachim M. Buhmann, Carlo Tomasi, Yossi Rubner, 2001, Comput. Vis. Image Underst..

Learning Top-Down Grouping of Compositional Hierarchies for Recognition

Joachim M. Buhmann, Björn Ommer, Michael Sauter, 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).

Landscape of clustering algorithms

Joachim M. Buhmann, Anil K. Jain, Alexander P. Topchy, 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

Non-monotone Continuous DR-submodular Maximization: Structure and Algorithms

Andreas Krause, Joachim M. Buhmann, Kfir Y. Levy, 2017, NIPS.

Weakly supervised structured output learning for semantic segmentation

Joachim M. Buhmann, Vittorio Ferrari, Alexander Vezhnevets, 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

Feature selection for support vector machines

Joachim M. Buhmann, Lothar Hermes, J. Buhmann, 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

Empirical evaluation of dissimilarity measures for color and texture

Joachim M. Buhmann, Carlo Tomasi, Yossi Rubner, 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

Continuous Submodular Function Maximization pdf

Joachim M. Buhmann, Andreas Krause, Yatao Bian, 2020, ArXiv.

Near-optimal experimental design for model selection in systems biology

Joachim M. Buhmann, Cheng Soon Ong, Mikael Sunnåker, 2013, Bioinform..

Variational Federated Multi-Task Learning pdf

Joachim M. Buhmann, Luca Corinzia, J. Buhmann, 2019, ArXiv.

Unsupervised segmentation of textured images by pairwise data clustering

Joachim M. Buhmann, Thomas Hofmann, Jan Puzicha, 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

A Hidden Markov Model for de Novo Peptide Sequencing

Joachim M. Buhmann, Volker Roth, Peter Widmayer, 2004, NIPS.

PerformancePrediction Challenge

Joachim M. Buhmann, Isabelle Guyon, Gideon Dror, 2006, IJCNN.

Topology Free Hidden Markov Models: Application to Background Modeling

Joachim M. Buhmann, Nikos Paragios, Björn Stenger, 2001, ICCV.

A probabilistic approach to hybrid role mining

Joachim M. Buhmann, David A. Basin, Mario Frank, 2009, CCS.

Speech enhancement with sparse coding in learned dictionaries

Joachim M. Buhmann, Christian D. Sigg, Tomas Dikk, 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

A Supervised Learning Approach for Crohn's Disease Detection Using Higher-Order Image Statistics and a Novel Shape Asymmetry Measure

Joachim M. Buhmann, Frans Vos, Dwarikanath Mahapatra, 2013, Journal of Digital Imaging.

Active learning based segmentation of Crohn's disease using principles of visual saliency

Joachim M. Buhmann, Frans Vos, Dwarikanath Mahapatra, 2014, 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI).

How informative are Minimum Spanning Tree algorithms?

Joachim M. Buhmann, Alexey Gronskiy, 2014, 2014 IEEE International Symposium on Information Theory.

Noise-driven temporal association in neural networks

Joachim M. Buhmann, Klaus Schulten, J. Buhmann, 1987 .

Size and distortion invariant object recognition by hierarchical graph matching

Joachim M. Buhmann, Christoph von der Malsburg, Martin Lades, 1990, 1990 IJCNN International Joint Conference on Neural Networks.

An optimization approach to unsupervised hierarchical texture segmentation

Joachim M. Buhmann, Thomas Hofmann, Jan Puzicha, 1997, Proceedings of International Conference on Image Processing.

A theory of proximity based clustering: structure detection by optimization

Joachim M. Buhmann, Thomas Hofmann, Jan Puzicha, 2000, Pattern Recognit..

Ellipsoidal Multiple Instance Learning

Joachim M. Buhmann, Cheng Soon Ong, Gabriel Krummenacher, 2013, ICML.

Entrack: A Data-Driven Maximum-Entropy Approach to Fiber Tractography

Joachim M. Buhmann, Viktor Wegmayr, Giacomo Giuliari, 2019, GCPR.

Boundary-constrained agglomerative segmentation

Joachim M. Buhmann, Lothar Hermes, J. Buhmann, 2004, IEEE Transactions on Geoscience and Remote Sensing.

A Supervised Learning Based Approach to Detect Crohn's Disease in Abdominal MR Volumes

Joachim M. Buhmann, Frans Vos, Dwarikanath Mahapatra, 2012, Abdominal Imaging.

Parametric Distributional Clustering for Image Segmentation

Joachim M. Buhmann, Thomas Zöller, Lothar Hermes, 2002, ECCV.

Shape constrained image segmentation by parametric distributional clustering

Joachim M. Buhmann, Thomas Zöller, 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

Seeing the Objects Behind the Dots: Recognition in Videos from a Moving Camera

Joachim M. Buhmann, Björn Ommer, Theodor Mader, 2009, International Journal of Computer Vision.

Guaranteed Non-convex Optimization: Submodular Maximization over Continuous Domains

Andreas Krause, Joachim M. Buhmann, Baharan Mirzasoleiman, 2016, AISTATS.

Data clustering and learning

Joachim M. Buhmann, J. Buhmann, 1998 .

Regularizing phase-based stereo

Joachim M. Buhmann, Thorsten Fröhlinghaus, J. Buhmann, 1996, Proceedings of 13th International Conference on Pattern Recognition.

Selecting the rank of truncated SVD by maximum approximation capacity

Joachim M. Buhmann, Mario Frank, J. Buhmann, 2011, 2011 IEEE International Symposium on Information Theory Proceedings.

Information Theoretic Model Validation for Spectral Clustering

Joachim M. Buhmann, Morteza Haghir Chehreghani, Alberto Giovanni Busetto, 2012, AISTATS.

Towards weakly supervised semantic segmentation by means of multiple instance and multitask learning

Joachim M. Buhmann, Alexander Vezhnevets, J. Buhmann, 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

Sensory segmentation with coupled neural oscillators

Joachim M. Buhmann, Christoph von der Malsburg, C. Malsburg, 1992, Biological Cybernetics.

Adaptive bandwidth selection for biomarker discovery in mass spectrometry

Joachim M. Buhmann, Volker Roth, Bernd Fischer, 2009, Artif. Intell. Medicine.

Posterior agreement for large parameter-rich optimization problems

Joachim M. Buhmann, Wojciech Szpankowski, Alexey Gronskiy, 2018, Theor. Comput. Sci..

Localizing and segmenting Crohn's disease affected regions in abdominal MRI using novel context features

Joachim M. Buhmann, Frans Vos, Dwarikanath Mahapatra, 2013, Medical Imaging.

Optimal DR-Submodular Maximization and Applications to Provable Mean Field Inference pdf

Andreas Krause, Joachim M. Buhmann, An Bian, 2018, ICML 2019.

Randomized Tree Ensembles for Object Detection in Computational Pathology

Joachim M. Buhmann, Peter J. Wild, Thomas J. Fuchs, 2009, ISVC.

Nonparametric Bayesian Image Segmentation

Joachim M. Buhmann, Peter Orbanz, J. Buhmann, 2008, International Journal of Computer Vision.

SAR images as mixtures of Gaussian mixtures

Joachim M. Buhmann, Peter Orbanz, J. Buhmann, 2005, IEEE International Conference on Image Processing 2005.

MRI-Based Surgical Planning for Lumbar Spinal Stenosis

Stefan Bauer, Joachim M. Buhmann, Gabriele Abbati, 2017, MICCAI.

Going Metric: Denoising Pairwise Data

Joachim M. Buhmann, Volker Roth, Klaus-Robert Müller, 2002, NIPS.

Neuronale Netzwerke als assoziative Speicher und als Systeme zur Mustererkennung

Joachim M. Buhmann, Joachim M. Buhmann, 1988 .

The Noisy Euclidean Traveling Salesman Problem and Learning

Joachim M. Buhmann, Mikio L. Braun, 2001, NIPS.

Multidimensional Scaling by Deterministic Annealing

Joachim M. Buhmann, Hansjörg Klock, J. Buhmann, 1997, EMMCVPR.

Histogram clustering for unsupervised image segmentation

Joachim M. Buhmann, Thomas Hofmann, Jan Puzicha, 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

Disentangled State Space Representations pdf

Joachim M. Buhmann, Bernhard Schölkopf, Ðorðe Miladinovic, 2019, ArXiv.

Infinite mixture-of-experts model for sparse survival regression with application to breast cancer

Joachim M. Buhmann, Volker Roth, Edgar Dahl, 2010, BMC Bioinformatics.

From Sets to Multisets: Provable Variational Inference for Probabilistic Integer Submodular Models

Joachim M. Buhmann, Andreas Krause, Aytunc Sahin, 2020, ICML.

Modeling Engagement Dynamics in Spelling Learning

Joachim M. Buhmann, Markus H. Gross, Severin Klingler, 2011, AIED.

Stability-Based Model Order Selection in Clustering with Applications to Gene Expression Data

Joachim M. Buhmann, Volker Roth, Mikio L. Braun, 2002, ICANN.

Entropy and Margin Maximization for Structured Output Learning

Joachim M. Buhmann, Cheng Soon Ong, Patrick Pletscher, 2010, ECML/PKDD.

Predicting Graduate-level Performance from Undergraduate Achievements

Joachim M. Buhmann, Elias August, Kay Henning Brodersen, 2011, EDM.

Inferring Hierarchical Clustering Structures by Deterministic Annealing

Joachim M. Buhmann, Thomas Hofmann, 1996, KDD.

Regression DCM for fMRI

Joachim M. Buhmann, Adeel Razi, Karl J. Friston, 2017, NeuroImage.

Inversion of hierarchical Bayesian models using Gaussian processes

Joachim M. Buhmann, Eduardo A. Aponte, Klaas E. Stephan, 2015, NeuroImage.

Information Theoretic Model Selection for Pattern Analysis

Joachim M. Buhmann, Mario Frank, Morteza Haghir Chehreghani, 2011, ICML Unsupervised and Transfer Learning.

Computational Pathology Analysis of Tissue Microarrays Predicts Survival of Renal Clear Cell Carcinoma Patients

Joachim M. Buhmann, Peter J. Wild, Thomas J. Fuchs, 2008, MICCAI.

Path Based Pairwise Data Clustering with Application to Texture Segmentation

Joachim M. Buhmann, Thomas Zöller, Bernd Fischer, 2001, EMMCVPR.

On Learning Optimal Texture Edge Detectors.

Joachim M. Buhmann, Stefan Will, Jan Puzicha, 2000 .

Unsupervised Learning for Robust Texture Segmentation

Joachim M. Buhmann, Jan Puzicha, J. Buhmann, 1998, Theoretical Foundations of Computer Vision.

Sound Field Indicators for Hearing Activity and Reverberation Time Estimation in Hearing Instruments

Joachim M. Buhmann, Manuela Feilner, Andreas P. Streich, 2010 .

A method for compression of data

Joachim M. Buhmann, Ulrich Ramacher, Wolfgang Raab, 2000 .

Expectation-maximization for sparse and non-negative PCA

Joachim M. Buhmann, Christian D. Sigg, J. Buhmann, 2008, ICML '08.

Denoising and Dimension Reduction in Feature Space

Joachim M. Buhmann, Klaus-Robert Müller, Mikio L. Braun, 2006, NIPS.

Active Data Clustering

Joachim M. Buhmann, Thomas Hofmann, J. Buhmann, 1997, NIPS.

Cardiac LV and RV Segmentation Using Mutual Context Information

Joachim M. Buhmann, Dwarikanath Mahapatra, J. Buhmann, 2012, MLMI.

On Spatial Quantization of Color Images

Joachim M. Buhmann, Dieter W. Fellner, Marcus Held, 1998, ECCV.

Semi-automatic Crohn's Disease Severity Estimation on MR Imaging

Joachim M. Buhmann, Zhang Li, Frans Vos, 2014, ABDI@MICCAI.

Distortion Invariant Object Recognition in the Dynamic Link Architecture

Joachim M. Buhmann, Wolfgang Konen, Rolf P. Würtz, 1993, IEEE Trans. Computers.

Phase Transitions in Parameter Rich Optimization Problems

Joachim M. Buhmann, Wojciech Szpankowski, Alexey Gronskiy, 2017, ANALCO.

Unsupervised Mitral Valve Segmentation in Echocardiography with Neural Network Matrix Factorization

Joachim M. Buhmann, Francesco Maisano, Luca Corinzia, 2019, AIME.

Information-theoretic analysis of MaxCut algorithms

Joachim M. Buhmann, Alexey Gronskiy, Yatao Bian, 2016, 2016 Information Theory and Applications Workshop (ITA).

Information theoretic model validation for clustering

Joachim M. Buhmann, 2010, 2010 IEEE International Symposium on Information Theory.

Wheel Defect Detection With Machine Learning

Joachim M. Buhmann, Cheng Soon Ong, Gabriel Krummenacher, 2018, IEEE Transactions on Intelligent Transportation Systems.

On spatial quantization of color images

Joachim M. Buhmann, Dieter W. Fellner, Marcus Held, 2000, IEEE Trans. Image Process..

Video coding by region-based motion compensation and spatio-temporal wavelet transform

Joachim M. Buhmann, Andreas Polzer, Hansjörg Klock, 1997, Proceedings of International Conference on Image Processing.

Robust vector quantization by competitive learning

Joachim M. Buhmann, Thomas Hofmann, 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

Storing sequences of biased patterns in neural networks with stochastic dynamics

Joachim M. Buhmann, Klaus Schulten, J. Buhmann, 1988 .

Spatial Dependency Networks: Neural Layers for Improved Generative Image Modeling pdf

Joachim M. Buhmann, Jürgen Schmidhuber, Stefan Bauer, 2021, ICLR 2021 Poster.

Spatially Dependent U-Nets: Highly Accurate Architectures for Medical Imaging Segmentation pdf

Joachim M. Buhmann, Joao B. S. Carvalho, Joao A. Santinha, 2021, ArXiv.

Self-supervised representation learning for surgical activity recognition

Joachim M. Buhmann, Michael Bajka, Markus Oelhafen, 2021, International Journal of Computer Assisted Radiology and Surgery.

Spatial Dependency Networks: Neural Layers for Improved Generative Image Modeling pdf

Joachim M. Buhmann, Stefan Bauer, J. Schmidhuber, 2021, ICLR.

Inference Principles and Model Selection (Dagstuhl Seminar 01301)

Joachim M. Buhmann, Bernhard Schölkopf, B. Schölkopf, 2021 .

Unsupervised Learning (Dagstuhl Seminar 99121)

Joachim M. Buhmann, Wolfgang Maass, Naftali Tishby, 2021 .

A New Adaptive Algorithm for the Polygonization of Noisy Imagery

Joachim M. Buhmann, Lothar Hermes, J. Buhmann, 2001 .

Approximate Sorting of Preference Data

Joachim M. Buhmann, Morteza Haghir Chehreghani, Ludwig M. Busse, 2011 .

Fully Automatic Registration of Electron Microscopy Images with High and Low Resolution

Joachim M. Buhmann, Verena Kaynig, Roger Wepf, 2007, Microscopy and Microanalysis.

Deterministic Annealing: Fast Physical Heuristics for Real–Time Optimization of Large Systems

Joachim M. Buhmann, Thomas Hofmann, Jan Puzicha, 2007 .

Shape constrained image segmentation by parametric distributional clustering

Joachim M. Buhmann, Thomas Zöller, 2004, CVPR 2004.

A Seven-Marker Signature and Clinical Outcome in Malignant Melanoma: A Large-Scale Tissue-Microarray Study with Two Independent Patient Cohorts

Joachim M. Buhmann, Volker Roth, Peter J. Wild, 2012, PloS one.

Free Energy Rates for a Class of Very Noisy Optimization Problems

Joachim M. Buhmann, Wojciech Szpankowski, J. Buhmann, 2014 .

Image-based computational quantification and visualization of genetic alterations and tumour heterogeneity

Joachim M. Buhmann, Peter J. Wild, Maria Gabrani, 2016, Scientific Reports.

Model Selection for Gaussian Process Regression by Approximation Set Coding

Nico S. Gorbach, Joachim M. Buhmann, Stefan Bauer, 2016, 1610.00907.

Is There an Association Between Pain and Magnetic Resonance Imaging Parameters in Patients With Lumbar Spinal Stenosis?

Joachim M. Buhmann, Peter J. Schüffler, Gustav Andreisek, 2016, Spine.