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ORCIDJoachim M. Buhmann
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- affiliation: ETH Zurich, Switzerland
- affiliation: University of Bonn, Germany
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2020 – today
- 2021
[i26]Luca Corinzia, Paolo Penna, Wojciech Szpankowski, Joachim M. Buhmann:
On maximum-likelihood estimation in the all-or-nothing regime. CoRR abs/2101.09994 (2021)- 2020
- [j66]Luca Corinzia, Fabian Laumer, Alessandro Candreva, Maurizio Taramasso, Francesco Maisano, Joachim M. Buhmann:
Neural collaborative filtering for unsupervised mitral valve segmentation in echocardiography. Artif. Intell. Medicine 110: 101975 (2020) - [c178]Patrick Schwab, Lorenz Linhardt, Stefan Bauer, Joachim M. Buhmann, Walter Karlen:
Learning Counterfactual Representations for Estimating Individual Dose-Response Curves. AAAI 2020: 5612-5619 - [c177]Aytunc Sahin, Yatao Bian, Joachim M. Buhmann, Andreas Krause:
From Sets to Multisets: Provable Variational Inference for Probabilistic Integer Submodular Models. ICML 2020: 8388-8397 - [c176]Viktor Wegmayr, Aytunc Sahin, Björn Sæmundsson, Joachim M. Buhmann:
Instance Segmentation for the Quantification of Microplastic Fiber Images. WACV 2020: 2199-2206 - [i25]Aytunc Sahin, Yatao Bian, Joachim M. Buhmann, Andreas Krause:
From Sets to Multisets: Provable Variational Inference for Probabilistic Integer Submodular Models. CoRR abs/2006.01293 (2020) - [i24]Yatao Bian, Joachim M. Buhmann, Andreas Krause:
Continuous Submodular Function Maximization. CoRR abs/2006.13474 (2020) - [i23]Luca Corinzia, Fabian Laumer, Alessandro Candreva, Maurizio Taramasso, Francesco Maisano, Joachim M. Buhmann:
Neural collaborative filtering for unsupervised mitral valve segmentation in echocardiography. CoRR abs/2008.05867 (2020) - [i22]Luca Corinzia, Paolo Penna, Wojciech Szpankowski, Joachim M. Buhmann:
Statistical and computational thresholds for the planted k-densest sub-hypergraph problem. CoRR abs/2011.11500 (2020)
2010 – 2019
- 2019
- [j65]Ðorðe Miladinovic, Christine Muheim
, Stefan Bauer, Andrea Spinnler, Daniela Noain, Mojtaba Bandarabadi, Benjamin Gallusser, Gabriel Krummenacher, Christian Baumann, Antoine Adamantidis, Steven A. Brown, Joachim M. Buhmann:
SPINDLE: End-to-end learning from EEG/EMG to extrapolate animal sleep scoring across experimental settings, labs and species. PLoS Comput. Biol. 15(4) (2019) - [c175]Luca Corinzia, Jesse Provost, Alessandro Candreva, Maurizio Tamarasso, Francesco Maisano, Joachim M. Buhmann:
Unsupervised Mitral Valve Segmentation in Echocardiography with Neural Network Matrix Factorization. AIME 2019: 410-419 - [c174]Philippe Wenk, Alkis Gotovos, Stefan Bauer, Nico S. Gorbach, Andreas Krause, Joachim M. Buhmann:
Fast Gaussian process based gradient matching for parameter identification in systems of nonlinear ODEs. AISTATS 2019: 1351-1360 - [c173]Viktor Wegmayr, Giacomo Giuliari, Joachim M. Buhmann:
Entrack: A Data-Driven Maximum-Entropy Approach to Fiber Tractography. GCPR 2019: 232-244 - [c172]Viktor Wegmayr, Maurice Hörold, Joachim M. Buhmann:
Generative Aging of Brain MR-Images and Prediction of Alzheimer Progression. GCPR 2019: 247-260 - [c171]Ðorðe Miladinovic, Muhammad Waleed Gondal, Bernhard Schölkopf, Joachim M. Buhmann, Stefan Bauer:
Disentangled State Space Models: Unsupervised Learning of dynamics across Heterogeneous Environments. DGS@ICLR 2019 - [c170]Yatao An Bian, Joachim M. Buhmann, Andreas Krause:
Optimal Continuous DR-Submodular Maximization and Applications to Provable Mean Field Inference. ICML 2019: 644-653 - [c169]Viktor Wegmayr, Maurice Hörold, Joachim M. Buhmann:
Generative Aging Of Brain MRI For Early Prediction Of MCI-AD Conversion. ISBI 2019: 1042-1046 - [c168]Luca Corinzia, Paolo Penna, Luca Mondada, Joachim M. Buhmann:
Exact Recovery for a Family of Community-Detection Generative Models. ISIT 2019: 415-419 - [i21]Luca Corinzia, Paolo Penna, Luca Mondada, Joachim M. Buhmann:
Exact Recovery for a Family of Community-Detection Generative Models. CoRR abs/1901.06799 (2019) - [i20]Patrick Schwab, Lorenz Linhardt, Stefan Bauer, Joachim M. Buhmann, Walter Karlen:
Learning Counterfactual Representations for Estimating Individual Dose-Response Curves. CoRR abs/1902.00981 (2019) - [i19]Ðorðe Miladinovic, Muhammad Waleed Gondal, Bernhard Schölkopf, Joachim M. Buhmann, Stefan Bauer:
Disentangled State Space Representations. CoRR abs/1906.03255 (2019) - [i18]Luca Corinzia, Joachim M. Buhmann:
Variational Federated Multi-Task Learning. CoRR abs/1906.06268 (2019) - 2018
- [j64]Joachim M. Buhmann, Alexey Gronskiy, Matús Mihalák, Tobias Pröger, Rastislav Srámek, Peter Widmayer:
Robust optimization in the presence of uncertainty: A generic approach. J. Comput. Syst. Sci. 94: 135-166 (2018) - [j63]Stefan Frässle, Ekaterina I. Lomakina, Lars Kasper, Zina M. Manjaly, Alexander P. Leff, Klaas P. Pruessmann, Joachim M. Buhmann, Klaas E. Stephan:
A generative model of whole-brain effective connectivity. NeuroImage 179: 505-529 (2018) - [j62]Nico S. Gorbach, Marc Tittgemeyer, Joachim M. Buhmann:
Pipeline validation for connectivity-based cortex parcellation. NeuroImage 181: 219-234 (2018) - [j61]Joachim M. Buhmann, Julien Dumazert, Alexey Gronskiy, Wojciech Szpankowski:
Posterior agreement for large parameter-rich optimization problems. Theor. Comput. Sci. 745: 1-22 (2018) - [j60]Gabriel Krummenacher, Cheng Soon Ong, Stefan Koller, Seijin Kobayashi, Joachim M. Buhmann:
Wheel Defect Detection With Machine Learning. IEEE Trans. Intell. Transp. Syst. 19(4): 1176-1187 (2018) - [c167]Joachim M. Buhmann:
VIS Capstone Address : Can I believe what I see?-Information theoretic algorithm validation. VAST 2018: 1 - [c166]Viktor Wegmayr, Giacomo Giuliari, Stefan Holdener, Joachim M. Buhmann:
Data-driven fiber tractography with neural networks. ISBI 2018: 1030-1033 - [c165]Alexey Gronskiy, Joachim M. Buhmann, Wojciech Szpankowski:
Free Energy Asymptotics for Problems with Weak Solution Dependencies. ISIT 2018: 2132-2136 - [c164]Viktor Wegmayr, Sai Aitharaju, Joachim M. Buhmann:
Classification of brain MRI with big data and deep 3D convolutional neural networks. Medical Imaging: Computer-Aided Diagnosis 2018: 105751S - [i17]Philippe Wenk, Alkis Gotovos, Stefan Bauer, Nico S. Gorbach, Andreas Krause, Joachim M. Buhmann:
Fast Gaussian Process Based Gradient Matching for Parameter Identification in Systems of Nonlinear ODEs. CoRR abs/1804.04378 (2018) - [i16]An Bian, Joachim M. Buhmann, Andreas Krause:
Optimal DR-Submodular Maximization and Applications to Provable Mean Field Inference. CoRR abs/1805.07482 (2018) - 2017
- [j59]Julian G. Zilly, Joachim M. Buhmann, Dwarikanath Mahapatra:
Glaucoma detection using entropy sampling and ensemble learning for automatic optic cup and disc segmentation. Comput. Medical Imaging Graph. 55: 28-41 (2017) - [j58]Stefan Frässle, Ekaterina I. Lomakina, Adeel Razi, Karl J. Friston, Joachim M. Buhmann, Klaas E. Stephan:
Regression DCM for fMRI. NeuroImage 155: 406-421 (2017) - [c163]Andrew An Bian, Baharan Mirzasoleiman, Joachim M. Buhmann, Andreas Krause:
Guaranteed Non-convex Optimization: Submodular Maximization over Continuous Domains. AISTATS 2017: 111-120 - [c162]Joachim M. Buhmann, Julien Dumazert, Alexey Gronskiy, Wojciech Szpankowski:
Phase Transitions in Parameter Rich Optimization Problems. ANALCO 2017: 148-155 - [c161]Nico S. Gorbach, Andrew An Bian, Benjamin Fischer, Stefan Bauer, Joachim M. Buhmann:
Model Selection for Gaussian Process Regression. GCPR 2017: 306-318 - [c160]Andrew An Bian, Joachim M. Buhmann, Andreas Krause, Sebastian Tschiatschek:
Guarantees for Greedy Maximization of Non-submodular Functions with Applications. ICML 2017: 498-507 - [c159]Gabriele Abbati, Stefan Bauer, Sebastian Winklhofer, Peter J. Schüffler, Ulrike Held, Jakob M. Burgstaller, Johann Steurer, Joachim M. Buhmann:
MRI-Based Surgical Planning for Lumbar Spinal Stenosis. MICCAI (3) 2017: 116-124 - [c158]An Bian, Kfir Yehuda Levy, Andreas Krause, Joachim M. Buhmann:
Non-monotone Continuous DR-submodular Maximization: Structure and Algorithms. NIPS 2017: 486-496 - [c157]Nico S. Gorbach, Stefan Bauer, Joachim M. Buhmann:
Scalable Variational Inference for Dynamical Systems. NIPS 2017: 4806-4815 - [c156]Stefan Bauer, Nico S. Gorbach, Ðorðe Miladinovic, Joachim M. Buhmann:
Efficient and Flexible Inference for Stochastic Systems. NIPS 2017: 6988-6998 - [i15]Andrew An Bian, Joachim M. Buhmann, Andreas Krause, Sebastian Tschiatschek:
Guarantees for Greedy Maximization of Non-submodular Functions with Applications. CoRR abs/1703.02100 (2017) - [i14]An Bian, Kfir Y. Levy, Andreas Krause, Joachim M. Buhmann:
Non-monotone Continuous DR-submodular Maximization: Structure and Algorithms. CoRR abs/1711.02515 (2017) - 2016
- [j57]Dwarikanath Mahapatra, Franciscus M. Vos, Joachim M. Buhmann:
Active learning based segmentation of Crohns disease from abdominal MRI. Comput. Methods Programs Biomed. 128: 75-85 (2016) - [c155]Dmitry Laptev, Nikolay Savinov, Joachim M. Buhmann, Marc Pollefeys:
TI-POOLING: Transformation-Invariant Pooling for Feature Learning in Convolutional Neural Networks. CVPR 2016: 289-297 - [c154]Yatao Bian, Alexey Gronskiy, Joachim M. Buhmann:
Information-theoretic analysis of MaxCut algorithms. ITA 2016: 1-5 - [c153]Gabriel Krummenacher, Brian McWilliams, Yannic Kilcher, Joachim M. Buhmann, Nicolai Meinshausen:
Scalable Adaptive Stochastic Optimization Using Random Projections. NIPS 2016: 1750-1758 - [i13]Thomas J. Fuchs, Joachim M. Buhmann:
Computational Pathology: Challenges and Promises for Tissue Analysis. CoRR abs/1601.00027 (2016) - [i12]Dmitry Laptev, Nikolay Savinov, Joachim M. Buhmann, Marc Pollefeys:
TI-POOLING: transformation-invariant pooling for feature learning in Convolutional Neural Networks. CoRR abs/1604.06318 (2016) - [i11]Stefan Bauer, Nicolas Carion, Peter J. Schüffler, Thomas J. Fuchs, Peter J. Wild, Joachim M. Buhmann:
Multi-Organ Cancer Classification and Survival Analysis. CoRR abs/1606.00897 (2016) - [i10]Andrew An Bian, Baharan Mirzasoleiman, Joachim M. Buhmann, Andreas Krause:
Guaranteed Non-convex Optimization: Submodular Maximization over Continuous Domains. CoRR abs/1606.05615 (2016) - [i9]Yatao Bian, Alexey Gronskiy, Joachim M. Buhmann:
Greedy MAXCUT Algorithms and their Information Content. CoRR abs/1609.00810 (2016) - [i8]Gabriel Krummenacher, Brian McWilliams, Yannic Kilcher, Joachim M. Buhmann, Nicolai Meinshausen:
Scalable Adaptive Stochastic Optimization Using Random Projections. CoRR abs/1611.06652 (2016) - 2015
- [j56]Andreas P. Streich, Joachim M. Buhmann:
Asymptotic analysis of estimators on multi-label data. Mach. Learn. 99(3): 373-409 (2015) - [j55]Ekaterina I. Lomakina, Saee Paliwal, Andreea Oliviana Diaconescu, Kay Henning Brodersen, Eduardo A. Aponte, Joachim M. Buhmann, Klaas E. Stephan:
Inversion of hierarchical Bayesian models using Gaussian processes. NeuroImage 118: 133-145 (2015) - [c152]David Balduzzi, Hastagiri Vanchinathan, Joachim M. Buhmann:
Kickback Cuts Backprop's Red-Tape: Biologically Plausible Credit Assignment in Neural Networks. AAAI 2015: 485-491 - [c151]Dmitry Laptev, Joachim M. Buhmann:
Transformation-Invariant Convolutional Jungles. CVPR 2015: 3043-3051 - [c150]Dwarikanath Mahapatra, Joachim M. Buhmann:
A field of experts model for optic cup and disc segmentation from retinal fundus images. ISBI 2015: 218-221 - [c149]Dwarikanath Mahapatra, Peter J. Schüffler, Frans Vos, Joachim M. Buhmann:
Crohn's disease segmentation from MRI using learned image priors. ISBI 2015: 625-628 - [c148]Dwarikanath Mahapatra, Zhang Li, Frans Vos, Joachim M. Buhmann:
Joint segmentation and groupwise registration of cardiac DCE MRI using sparse data representations. ISBI 2015: 1312-1315 - [c147]Yatao Bian, Alexey Gronskiy, Joachim M. Buhmann:
Greedy MaxCut algorithms and their information content. ITW 2015: 1-5 - [c146]Dwarikanath Mahapatra, Joachim M. Buhmann:
Visual Saliency Based Active Learning for Prostate MRI Segmentation. MLMI 2015: 9-16 - [c145]Julian G. Zilly, Joachim M. Buhmann, Dwarikanath Mahapatra:
Boosting Convolutional Filters with Entropy Sampling for Optic Cup and Disc Image Segmentation from Fundus Images. MLMI 2015: 136-143 - 2014
- [j54]Dwarikanath Mahapatra, Joachim M. Buhmann:
Prostate MRI Segmentation Using Learned Semantic Knowledge and Graph Cuts. IEEE Trans. Biomed. Eng. 61(3): 756-764 (2014) - [c144]Dmitry Laptev, Joachim M. Buhmann:
Convolutional Decision Trees for Feature Learning and Segmentation. GCPR 2014: 95-106 - [c143]Dmitry Laptev, Joachim M. Buhmann:
SuperSlicing Frame Restoration for Anisotropic ssTEM and Video Data. Neural Connectomics 2014: 91-101 - [c142]Dwarikanath Mahapatra, Peter J. Schüffler, Jeroen A. W. Tielbeek, Jesica Makanyanga, Jaap Stoker, Stuart A. Taylor, Franciscus M. Vos, Joachim M. Buhmann:
Active learning based segmentation of Crohn's disease using principles of visual saliency. ISBI 2014: 226-229 - [c141]Dmitry Laptev, A. Veznevets, Joachim M. Buhmann:
Superslicing frame restoration for anisotropic sstem. ISBI 2014: 1198-1201 - [c140]Guangyao Zhou, Stuart Geman, Joachim M. Buhmann:
Sparse feature selection by information theory. ISIT 2014: 926-930 - [c139]Alexey Gronskiy, Joachim M. Buhmann:
How informative are Minimum Spanning Tree algorithms? ISIT 2014: 2277-2281 - [c138]Peter J. Schüffler, Dwarikanath Mahapatra, Robiel Naziroglu, Zhang Li, Carl A. J. Puylaert, Rado Andriantsimiavona, Franciscus M. Vos, Doug A. Pendsé, C. Yung Nio, Jaap Stoker, Stuart A. Taylor, Joachim M. Buhmann:
Semi-automatic Crohn's Disease Severity Estimation on MR Imaging. ABDI@MICCAI 2014: 128-138 - [c137]Dwarikanath Mahapatra, Peter J. Schüffler, Jeroen A. W. Tielbeek, Carl A. J. Puylaert, Jesica C. Makanyanga, Alex Menys, Rado Andriantsimiavona, Jaap Stoker, Stuart A. Taylor, Franciscus M. Vos, Joachim M. Buhmann:
Combining Multiple Expert Annotations Using Semi-supervised Learning and Graph Cuts for Crohn's Disease Segmentation. ABDI@MICCAI 2014: 139-147 - [c136]Brian McWilliams, Gabriel Krummenacher, Mario Lucic, Joachim M. Buhmann:
Fast and Robust Least Squares Estimation in Corrupted Linear Models. NIPS 2014: 415-423 - [i7]David Balduzzi, Hastagiri Vanchinathan, Joachim M. Buhmann:
Kickback cuts Backprop's red-tape: Biologically plausible credit assignment in neural networks. CoRR abs/1411.6191 (2014) - 2013
- [j53]Tanja Käser, Gian-Marco Baschera, Alberto Giovanni Busetto, Severin Klingler, Barbara Solenthaler, Joachim M. Buhmann, Markus H. Gross:
Towards a Framework for Modelling Engagement Dynamics in Multiple Learning Domains. Int. J. Artif. Intell. Educ. 22(1-2): 59-83 (2013) - [j52]Alberto Giovanni Busetto, Alain Hauser, Gabriel Krummenacher, Mikael Sunnåker, Sotiris Dimopoulos, Cheng Soon Ong, Jörg Stelling, Joachim M. Buhmann:
Near-optimal experimental design for model selection in systems biology. Bioinform. 29(20): 2625-2632 (2013) - [j51]Dwarikanath Mahapatra, Peter Schueffler, Jeroen A. W. Tielbeek, Joachim M. Buhmann, Franciscus M. Vos:
A Supervised Learning Approach for Crohn's Disease Detection Using Higher-Order Image Statistics and a Novel Shape Asymmetry Measure. J. Digit. Imaging 26(5): 920-931 (2013) - [j50]Kay Henning Brodersen, Jean Daunizeau, Christoph Mathys, Justin R. Chumbley, Joachim M. Buhmann, Klaas E. Stephan:
Variational Bayesian mixed-effects inference for classification studies. NeuroImage 76: 345-361 (2013) - [j49]Mario Frank, Joachim M. Buhmann, David A. Basin:
Role Mining with Probabilistic Models. ACM Trans. Inf. Syst. Secur. 15(4): 15:1-15:28 (2013) - [j48]Dwarikanath Mahapatra, Peter J. Schüffler, Jeroen A. W. Tielbeek, Jesica Makanyanga, Jaap Stoker, Stuart A. Taylor, Franciscus M. Vos, Joachim M. Buhmann:
Automatic Detection and Segmentation of Crohn's Disease Tissues From Abdominal MRI. IEEE Trans. Medical Imaging 32(12): 2332-2347 (2013) - [c135]Ludwig M. Busse, Morteza Haghir Chehreghani, Joachim M. Buhmann:
Approximate Sorting. GCPR 2013: 142-152 - [c134]Gabriel Krummenacher, Cheng Soon Ong, Joachim M. Buhmann:
Ellipsoidal Multiple Instance Learning. ICML (2) 2013: 73-81 - [c133]Joachim M. Buhmann, Matús Mihalák, Rastislav Srámek, Peter Widmayer:
Robust optimization in the presence of uncertainty. ITCS 2013: 505-514 - [c132]Dwarikanath Mahapatra, Peter J. Schüffler, Jeroen A. W. Tielbeek, Franciscus M. Vos, Joachim M. Buhmann:
Crohn's disease tissue segmentation from abdominal MRI using semantic information and graph cuts. ISBI 2013: 358-361 - [c131]Dwarikanath Mahapatra, Alexander Vezhnevets, Peter J. Schüffler, Jeroen A. W. Tielbeek, Franciscus M. Vos, Joachim M. Buhmann:
Weakly supervised semantic segmentation of Crohn's disease tissues from abdominal MRI. ISBI 2013: 844-847 - [c130]Dwarikanath Mahapatra, Joachim M. Buhmann:
Automatic cardiac RV segmentation using semantic information with graph cuts. ISBI 2013: 1106-1109 - [c129]Peter J. Schüffler, Dwarikanath Mahapatra, Jeroen A. W. Tielbeek, Franciscus M. Vos, Jesica Makanyanga, Doug Pendsé, C. Yung Nio, Jaap Stoker, Stuart A. Taylor, Joachim M. Buhmann:
A Model Development Pipeline for Crohn's Disease Severity Assessment from Magnetic Resonance Images. Abdominal Imaging 2013: 1-10 - [c128]Dwarikanath Mahapatra, Peter J. Schüffler, Jeroen A. W. Tielbeek, Franciscus M. Vos, Joachim M. Buhmann:
Semi-Supervised and Active Learning for Automatic Segmentation of Crohn's Disease. MICCAI (2) 2013: 214-221 - [c127]Dwarikanath Mahapatra, Peter J. Schüffler, Jeroen A. W. Tielbeek, Franciscus M. Vos, Joachim M. Buhmann:
Localizing and segmenting Crohn's disease affected regions in abdominal MRI using novel context features. Medical Imaging: Image Processing 2013: 86693K - [c126]Brian McWilliams, David Balduzzi, Joachim M. Buhmann:
Correlated random features for fast semi-supervised learning. NIPS 2013: 440-448 - [p4]Joachim M. Buhmann:
SIMBAD: Emergence of Pattern Similarity. Similarity-Based Pattern Analysis and Recognition 2013: 45-64 - [p3]Volker Roth, Thomas J. Fuchs, Julia E. Vogt, Sandhya Prabhakaran, Joachim M. Buhmann:
Structure Preserving Embedding of Dissimilarity Data. Similarity-Based Pattern Analysis and Recognition 2013: 157-177 - [p2]Peter J. Schüffler, Thomas J. Fuchs, Cheng Soon Ong, Volker Roth, Joachim M. Buhmann:
Automated Analysis of Tissue Micro-Array Images on the Example of Renal Cell Carcinoma. Similarity-Based Pattern Analysis and Recognition 2013: 219-245 - [i6]Brian McWilliams, David Balduzzi, Joachim M. Buhmann:
Correlated random features for fast semi-supervised learning. CoRR abs/1306.5554 (2013) - 2012
- [j47]Mario Frank, Andreas P. Streich, David A. Basin, Joachim M. Buhmann:
Multi-Assignment Clustering for Boolean Data. J. Mach. Learn. Res. 13: 459-489 (2012) - [j46]Kay Henning Brodersen, Christoph Mathys, Justin R. Chumbley, Jean Daunizeau, Cheng Soon Ong, Joachim M. Buhmann, Klaas E. Stephan:
Bayesian mixed-effects inference on classification performance in hierarchical data sets. J. Mach. Learn. Res. 13: 3133-3176 (2012) - [j45]Kay Henning Brodersen, Katja Wiech, Ekaterina I. Lomakina, Chia-shu Lin, Joachim M. Buhmann, Ulrike Bingel, Markus Ploner, Klaas Enno Stephan, Irene Tracey:
Decoding the perception of pain from fMRI using multivariate pattern analysis. NeuroImage 63(3): 1162-1170 (2012) - [j44]Christian D. Sigg, Tomas Dikk, Joachim M. Buhmann:
Learning Dictionaries With Bounded Self-Coherence. IEEE Signal Process. Lett. 19(12): 861-864 (2012) - [j43]Christian D. Sigg, Tomas Dikk, Joachim M. Buhmann:
Speech Enhancement Using Generative Dictionary Learning. IEEE Trans. Speech Audio Process. 20(6): 1698-1712 (2012) - [c125]Alexander Vezhnevets, Vittorio Ferrari, Joachim M. Buhmann:
Weakly supervised structured output learning for semantic segmentation. CVPR 2012: 845-852 - [c124]Alexander Vezhnevets, Joachim M. Buhmann, Vittorio Ferrari:
Active learning for semantic segmentation with expected change. CVPR 2012: 3162-3169 - [c123]Franciscus M. Vos, Jeroen A. W. Tielbeek, Robiel E. Naziroglu, Zhang Li, Peter Schueffler, Dwarikanath Mahapatra, Alexander Wiebel, Cristina Lavini, Joachim M. Buhmann, Hans-Christian Hege, Jaap Stoker, Lucas J. van Vliet:
Computational modeling for assessment of IBD: To be or not to be? EMBC 2012: 3974-3977 - [c122]Joachim M. Buhmann:
Context Sensitive Information: Model Validation by Information Theory. ICPRAM (1) 2012 - [c121]Ludwig M. Busse, Morteza Haghir Chehreghani, Joachim M. Buhmann:
The information content in sorting algorithms. ISIT 2012: 2746-2750 - [c120]Dwarikanath Mahapatra, Peter Schueffler, Jeroen A. W. Tielbeek, Joachim M. Buhmann, Franciscus M. Vos:
A Supervised Learning Based Approach to Detect Crohn's Disease in Abdominal MR Volumes. Abdominal Imaging 2012: 97-106 - [c119]Dwarikanath Mahapatra, Joachim M. Buhmann:
Cardiac LV and RV Segmentation Using Mutual Context Information. MLMI 2012: 201-209 - [c118]Dmitry Laptev, Alexander Vezhnevets, Sarvesh Dwivedi, Joachim M. Buhmann:
Anisotropic ssTEM Image Segmentation Using Dense Correspondence across Sections. MICCAI (1) 2012: 323-330 - [c117]Joachim M. Buhmann, Morteza Haghir Chehreghani, Mario Frank, Andreas P. Streich:
Information Theoretic Model Selection for Pattern Analysis. ICML Unsupervised and Transfer Learning 2012: 51-64 - [c116]Morteza Haghir Chehreghani, Alberto Giovanni Busetto, Joachim M. Buhmann:
Information Theoretic Model Validation for Spectral Clustering. AISTATS 2012: 495-503 - [i5]Christian D. Sigg, Tomas Dikk, Joachim M. Buhmann:
Learning Dictionaries with Bounded Self-Coherence. CoRR abs/1205.6210 (2012) - [i4]Mario Frank, Joachim M. Buhmann, David A. Basin:
Role Mining with Probabilistic Models. CoRR abs/1212.4775 (2012) - 2011
- [j42]Thomas J. Fuchs, Joachim M. Buhmann:
Computational pathology: Challenges and promises for tissue analysis. Comput. Medical Imaging Graph. 35(7-8): 515-530 (2011) - [j41]Manfred Claassen, Ruedi Aebersold, Joachim M. Buhmann:
Proteome Coverage Prediction for Integrated Proteomics Datasets. J. Comput. Biol. 18(3): 283-293 (2011) - [j40]Kay Henning Brodersen, Florent Haiss, Cheng Soon Ong, Fabienne Jung, Marc Tittgemeyer, Joachim M. Buhmann, Bruno Weber, Klaas E. Stephan:
Model-based feature construction for multivariate decoding. NeuroImage 56(2): 601-615 (2011) - [j39]Kay Henning Brodersen, Thomas M. Schofield, Alexander P. Leff, Cheng Soon Ong, Ekaterina I. Lomakina, Joachim M. Buhmann, Klaas E. Stephan:
Generative Embedding for Model-Based Classification of fMRI Data. PLoS Comput. Biol. 7(6) (2011) - [c115]Gian-Marco Baschera, Alberto Giovanni Busetto, Severin Klingler, Joachim M. Buhmann, Markus H. Gross:
Modeling Engagement Dynamics in Spelling Learning. AIED 2011: 31-38 - [c114]Alexander Vezhnevets, Joachim M. Buhmann:
Agnostic Domain Adaptation. DAGM-Symposium 2011: 376-385 - [c113]Judith Zimmermann, Kay Henning Brodersen, Jean-Philippe Pellet, Elias August, Joachim M. Buhmann:
Predicting Graduate-level Performance from Undergraduate Achievements. EDM 2011: 357-358 - [c112]Alexander Vezhnevets, Vittorio Ferrari, Joachim M. Buhmann:
Weakly supervised semantic segmentation with a multi-image model. ICCV 2011: 643-650 - [c111]Mario Frank, Joachim M. Buhmann:
Selecting the rank of truncated SVD by maximum approximation capacity. ISIT 2011: 1036-1040 - [c110]Joachim M. Buhmann:
Context Sensitive Information: Model Validation by Information Theory. MCPR 2011: 12-21 - [c109]Mario Frank, Morteza Haghir Chehreghani, Joachim M. Buhmann:
The Minimum Transfer Cost Principle for Model-Order Selection. ECML/PKDD (1) 2011: 423-438 - [c108]Ludwig M. Busse, Joachim M. Buhmann:
Model-Based Clustering of Inhomogeneous Paired Comparison Data. SIMBAD 2011: 207-221 - [i3]Mario Frank, Joachim M. Buhmann:
Selecting the rank of SVD by Maximum Approximation Capacity. CoRR abs/1102.3176 (2011) - 2010
- [j38]Sudhir Raman, Thomas J. Fuchs, Peter J. Wild, Edgar Dahl, Joachim M. Buhmann, Volker Roth:
Infinite mixture-of-experts model for sparse survival regression with application to breast cancer. BMC Bioinform. 11(S-8): S8 (2010) - [j37]