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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
- 2024
[j72]Ivan Ovinnikov
, Ami Beuret, Flavia Cavaliere, Joachim M. Buhmann:
Fundamentals of Arthroscopic Surgery Training and beyond: a reinforcement learning exploration and benchmark. Int. J. Comput. Assist. Radiol. Surg. 19(9): 1773-1781 (2024)- [i46]Xia Li, Fabian Zhang, Muheng Li, Damien C. Weber, Antony J. Lomax, Joachim M. Buhmann, Ye Zhang:
Neural Graphics Primitives-based Deformable Image Registration for On-the-fly Motion Extraction. CoRR abs/2402.05568 (2024) - [i45]Marc Bartholet, Taehyeon Kim, Ami Beuret, Se-Young Yun, Joachim M. Buhmann:
Non-linear Fusion in Federated Learning: A Hypernetwork Approach to Federated Domain Generalization. CoRR abs/2402.06974 (2024) - [i44]Mengyuan Liu, Zhongbin Fang, Xia Li, Joachim M. Buhmann, Xiangtai Li, Chen Change Loy:
Point-In-Context: Understanding Point Cloud via In-Context Learning. CoRR abs/2404.12352 (2024) - [i43]Xia Li, Muheng Li, Antony J. Lomax, Joachim M. Buhmann, Ye Zhang:
Continuous sPatial-Temporal Deformable Image Registration (CPT-DIR) for motion modelling in radiotherapy: beyond classic voxel-based methods. CoRR abs/2405.00430 (2024) - [i42]Xia Li, Runzhao Yang, Xiangtai Li, Antony J. Lomax, Ye Zhang, Joachim M. Buhmann:
CPT-Interp: Continuous sPatial and Temporal Motion Modeling for 4D Medical Image Interpolation. CoRR abs/2405.15385 (2024) - [i41]Peifeng Jiang, Hong Liu, Xia Li, Ti Wang, Fabian Zhang, Joachim M. Buhmann:
TAMBRIDGE: Bridging Frame-Centered Tracking and 3D Gaussian Splatting for Enhanced SLAM. CoRR abs/2405.19614 (2024) - [i40]Jihe Li, Fabian Zhang, Xia Li, Tianhao Zhang, Ye Zhang, Joachim M. Buhmann:
Gaussian Representation for Deformable Image Registration. CoRR abs/2406.03394 (2024) - [i39]Omar G. Younis, Luca Corinzia, Ioannis N. Athanasiadis, Andreas Krause, Joachim M. Buhmann, Matteo Turchetta:
Breeding Programs Optimization with Reinforcement Learning. CoRR abs/2406.03932 (2024) - [i38]Robin C. Geyer, Alessandro Torcinovich, João B. S. Carvalho, Alexander Meyer, Joachim M. Buhmann:
Measuring Orthogonality in Representations of Generative Models. CoRR abs/2407.03728 (2024) - [i37]Ivan Ovinnikov, Eugene Bykovets, Joachim M. Buhmann:
Learning Causally Invariant Reward Functions from Diverse Demonstrations. CoRR abs/2409.08012 (2024) - 2023
- [j71]Omar G. Younis, Matteo Turchetta, Daniel Ariza Suarez, Steven Yates, Bruno Studer, Ioannis N. Athanasiadis, Andreas Krause, Joachim M. Buhmann, Luca Corinzia:
ChromaX: a fast and scalable breeding program simulator. Bioinform. 39(12) (2023) - [j70]Fabian Laumer, Mounir Amrani, Laura Manduchi, Ami Beuret, Lena Rubi, Alina Dubatovka, Christian M. Matter, Joachim M. Buhmann:
Weakly supervised inference of personalized heart meshes based on echocardiography videos. Medical Image Anal. 83: 102653 (2023) - [j69]Simon Föll, Alina Dubatovka, Eugen Ernst, Siu Lun Chau, Martin Maritsch, Patrik Okanovic, Gudrun Thäter, Joachim M. Buhmann, Felix Wortmann, Krikamol Muandet:
Gated Domain Units for Multi-source Domain Generalization. Trans. Mach. Learn. Res. 2023 (2023) - [c189]João B. S. Carvalho, Carlos Cotrini, Fabian Laumer, André Euler, Katharina Martini, Thomas Frauenfelder, Joachim M. Buhmann:
Domain Generalization for Diagnosis of Pulmonary Fibrosis Using Dose-Invariant Feature Selection. ISBI 2023: 1-5 - [c188]João B. S. Carvalho, Mengtao Zhang, Robin Geyer, Carlos Cotrini, Joachim M. Buhmann:
Invariant Anomaly Detection under Distribution Shifts: A Causal Perspective. NeurIPS 2023 - [c187]Zhongbin Fang, Xiangtai Li, Xia Li, Joachim M. Buhmann, Chen Change Loy, Mengyuan Liu:
Explore In-Context Learning for 3D Point Cloud Understanding. NeurIPS 2023 - [i36]Zhongbin Fang, Xiangtai Li, Xia Li, Joachim M. Buhmann, Chen Change Loy, Mengyuan Liu:
Explore In-Context Learning for 3D Point Cloud Understanding. CoRR abs/2306.08659 (2023) - [i35]Lukas Klein, João B. S. Carvalho, Mennatallah El-Assady, Paolo Penna, Joachim M. Buhmann, Paul F. Jaeger:
Improving Explainability of Disentangled Representations using Multipath-Attribution Mappings. CoRR abs/2306.09035 (2023) - [i34]Ivan Ovinnikov, Joachim M. Buhmann:
Regularizing Adversarial Imitation Learning Using Causal Invariance. CoRR abs/2308.09189 (2023) - [i33]João B. S. Carvalho, Mengtao Zhang, Robin Geyer, Carlos Cotrini, Joachim M. Buhmann:
Invariant Anomaly Detection under Distribution Shifts: A Causal Perspective. CoRR abs/2312.14329 (2023) - 2022
- [c186]Luca Corinzia, Paolo Penna, Wojciech Szpankowski, Joachim M. Buhmann:
Statistical and computational thresholds for the planted k-densest sub-hypergraph problem. AISTATS 2022: 11615-11640 - [c185]João B. S. Carvalho, João A. Santinha, Djordje Miladinovic, Carlos Cotrini, Joachim M. Buhmann:
Holistic Modeling In Medical Image Segmentation Using Spatial Recurrence. MIDL 2022: 199-218 - [c184]Lukas Klein, João B. S. Carvalho, Mennatallah El-Assady, Paolo Penna, Joachim M. Buhmann, Paul F. Jaeger:
Improving Explainability of Disentangled Representations using Multipath-Attribution Mappings. MIDL 2022: 689-712 - [c183]Djordje Miladinovic, Kumar Shridhar, Kushal Jain, Max B. Paulus, Joachim M. Buhmann, Carl Allen:
Learning to Drop Out: An Adversarial Approach to Training Sequence VAEs. NeurIPS 2022 - [c182]Matteo Turchetta, Luca Corinzia, Scott Sussex, Amanda Burton, Juan Herrera, Ioannis Athanasiadis, Joachim M. Buhmann, Andreas Krause:
Learning Long-Term Crop Management Strategies with CyclesGym. NeurIPS 2022 - [i32]Simon Föll, Alina Dubatovka, Eugen Ernst, Martin Maritsch, Patrik Okanovic, Gudrun Thäter, Joachim M. Buhmann, Felix Wortmann, Krikamol Muandet:
Gated Domain Units for Multi-source Domain Generalization. CoRR abs/2206.12444 (2022) - [i31]Eugene Bykovets, Yannick Metz, Mennatallah El-Assady, Daniel A. Keim, Joachim M. Buhmann:
BARReL: Bottleneck Attention for Adversarial Robustness in Vision-Based Reinforcement Learning. CoRR abs/2208.10481 (2022) - [i30]Ðorðe Miladinovic, Kumar Shridhar, Kushal Jain, Max B. Paulus, Joachim M. Buhmann, Carl Allen:
Learning to Drop Out: An Adversarial Approach to Training Sequence VAEs. CoRR abs/2209.12590 (2022) - [i29]Eugene Bykovets, Yannick Metz, Mennatallah El-Assady, Daniel A. Keim, Joachim M. Buhmann:
How to Enable Uncertainty Estimation in Proximal Policy Optimization. CoRR abs/2210.03649 (2022) - 2021
- [j68]Daniel Paysan, Luis Haug, Michael Bajka, Markus Oelhafen, Joachim M. Buhmann:
Self-supervised representation learning for surgical activity recognition. Int. J. Comput. Assist. Radiol. Surg. 16(11): 2037-2044 (2021) - [j67]Viktor Wegmayr, Joachim M. Buhmann:
Entrack: Probabilistic Spherical Regression with Entropy Regularization for Fiber Tractography. Int. J. Comput. Vis. 129(3): 656-680 (2021) - [c181]Ðorðe Miladinovic, Aleksandar Stanic, Stefan Bauer, Jürgen Schmidhuber, Joachim M. Buhmann:
Spatial Dependency Networks: Neural Layers for Improved Generative Image Modeling. ICLR 2021 - [c180]Luca Corinzia, Paolo Penna, Wojciech Szpankowski, Joachim M. Buhmann:
On maximum-likelihood estimation in the all-or-nothing regime. ISIT 2021: 1106-1111 - [i28]Luca Corinzia, Paolo Penna, Wojciech Szpankowski, Joachim M. Buhmann:
On maximum-likelihood estimation in the all-or-nothing regime. CoRR abs/2101.09994 (2021) - [i27]Djordje Miladinovic, Aleksandar Stanic, Stefan Bauer, Jürgen Schmidhuber, Joachim M. Buhmann:
Spatial Dependency Networks: Neural Layers for Improved Generative Image Modeling. CoRR abs/2103.08877 (2021) - [i26]João B. S. Carvalho, João A. Santinha, Djordje Miladinovic, Joachim M. Buhmann:
Spatially Dependent U-Nets: Highly Accurate Architectures for Medical Imaging Segmentation. CoRR abs/2103.11713 (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) - [c179]Patrick Schwab, Lorenz Linhardt, Stefan Bauer, Joachim M. Buhmann, Walter Karlen:
Learning Counterfactual Representations for Estimating Individual Dose-Response Curves. AAAI 2020: 5612-5619 - [c178]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 - [c177]Fabian Laumer, Gabriel Fringeli, Alina Dubatovka, Laura Manduchi, Joachim M. Buhmann:
DeepHeartBeat: Latent trajectory learning of cardiac cycles using cardiac ultrasounds. ML4H@NeurIPS 2020: 194-212 - [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 R. 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]Björn Ommer, Joachim M. Buhmann:
Learning the Compositional Nature of Visual Object Categories for Recognition. IEEE Trans. Pattern Anal. Mach. Intell. 32(3): 501-516 (2010) - [c107]Verena Kaynig, Thomas J. Fuchs, Joachim M. Buhmann:
Neuron geometry extraction by perceptual grouping in ssTEM images. CVPR 2010: 2902-2909 - [c106]Alexander Vezhnevets, Joachim M. Buhmann:
Towards weakly supervised semantic segmentation by means of multiple instance and multitask learning. CVPR 2010: 3249-3256 - [c105]Peter J. Schüffler, Thomas J. Fuchs, Cheng Soon Ong, Volker Roth, Joachim M. Buhmann:
Computational TMA Analysis and Cell Nucleus Classification of Renal Cell Carcinoma. DAGM-Symposium 2010: 202-211 - [c104]Yvonne Moh, Joachim M. Buhmann:
Regularized online learning of pseudometrics. ICASSP 2010: 1990-1993 - [c103]Christian D. Sigg, Tomas Dikk, Joachim M. Buhmann:
Speech enhancement with sparse coding in learned dictionaries. ICASSP 2010: 4758-4761 - [c102]Kay Henning Brodersen, Cheng Soon Ong, Klaas Enno Stephan, Joachim M. Buhmann:
The Balanced Accuracy and Its Posterior Distribution. ICPR 2010: 3121-3124 - [c101]Kay Henning Brodersen, Cheng Soon Ong, Klaas Enno Stephan, Joachim M. Buhmann:
The Binormal Assumption on Precision-Recall Curves. ICPR 2010: 4263-4266 - [c100]Joachim M. Buhmann:
Information theoretic model validation for clustering. ISIT 2010: 1398-1402 - [c99]Verena Kaynig, Thomas J. Fuchs, Joachim M. Buhmann:
Geometrical Consistent 3D Tracing of Neuronal Processes in ssTEM Data. MICCAI (2) 2010: 209-216 - [c98]Patrick Pletscher, Cheng Soon Ong, Joachim M. Buhmann:
Entropy and Margin Maximization for Structured Output Learning. ECML/PKDD (3) 2010: 83-98 - [c97]Manfred Claassen, Ruedi Aebersold, Joachim M. Buhmann:
Proteome Coverage Prediction for Integrated Proteomics Datasets. RECOMB 2010: 96-109 - [c96]Mario Frank, Joachim M. Buhmann, David A. Basin:
On the definition of role mining. SACMAT 2010: 35-44 - [i2]Joachim M. Buhmann:
Information theoretic model validation for clustering. CoRR abs/1006.0375 (2010)
2000 – 2009
- 2009
- [j36]Bernd Fischer, Volker Roth, Joachim M. Buhmann:
Adaptive bandwidth selection for biomarker discovery in mass spectrometry. Artif. Intell. Medicine 45(2-3): 207-214 (2009) - [j35]Manfred Claassen, Ruedi Aebersold, Joachim M. Buhmann:
Proteome coverage prediction with infinite Markov models. Bioinform. 25(12) (2009) - [j34]Björn Ommer, Theodor Mader, Joachim M. Buhmann:
Seeing the Objects Behind the Dots: Recognition in Videos from a Moving Camera. Int. J. Comput. Vis. 83(1): 57-71 (2009) - [c95]Mario Frank, Andreas P. Streich, David A. Basin, Joachim M. Buhmann:
A probabilistic approach to hybrid role mining. CCS 2009: 101-111 - [c94]Alberto Giovanni Busetto, Joachim M. Buhmann:
Stable Bayesian Parameter Estimation for Biological Dynamical Systems. CSE (1) 2009: 148-157 - [c93]Yvonne Moh, Joachim M. Buhmann:
Manifold regularization for semi-supervised sequential learning. ICASSP 2009: 1617-1620 - [c92]Thomas J. Fuchs, Joachim M. Buhmann:
Inter-active learning of randomized tree ensembles for object detection. ICCV Workshops 2009: 1370-1377 - [c91]Alberto Giovanni Busetto, Cheng Soon Ong, Joachim M. Buhmann:
Optimized expected information gain for nonlinear dynamical systems. ICML 2009: 97-104 - [c90]Andreas P. Streich, Mario Frank, David A. Basin, Joachim M. Buhmann:
Multi-assignment clustering for Boolean data. ICML 2009: 969-976 - [c89]Thomas J. Fuchs, Johannes Haybaeck, Peter J. Wild, Mathias Heikenwalder, Holger Moch, Adriano Aguzzi, Joachim M. Buhmann:
Randomized Tree Ensembles for Object Detection in Computational Pathology. ISVC (1) 2009: 367-378 - [c88]Xenofon E. Floros, Thomas J. Fuchs, Markus P. Rechsteiner, Giatgen Spinas, Holger Moch, Joachim M. Buhmann:
Graph-Based Pancreatic Islet Segmentation for Early Type 2 Diabetes Mellitus on Histopathological Tissue. MICCAI (1) 2009: 633-640 - [c87]Alberto Giovanni Busetto, Joachim M. Buhmann:
Structure Identification by Optimized Interventions. AISTATS 2009: 57-64 - [c86]Patrick Pletscher, Cheng Soon Ong, Joachim M. Buhmann:
Spanning Tree Approximations for Conditional Random Fields. AISTATS 2009: 408-415 - 2008
- [j33]Peter Orbanz, Joachim M. Buhmann:
Nonparametric Bayesian Image Segmentation. Int. J. Comput. Vis. 77(1-3): 25-45 (2008) - [j32]Mikio L. Braun, Joachim M. Buhmann, Klaus-Robert Müller:
On Relevant Dimensions in Kernel Feature Spaces. J. Mach. Learn. Res. 9: 1875-1908 (2008) - [c85]Mario Frank, David A. Basin, Joachim M. Buhmann:
A class of probabilistic models for role engineering. CCS 2008: 299-310 - [c84]Verena Kaynig, Bernd Fischer, Joachim M. Buhmann:
Probabilistic image registration and anomaly detection by nonlinear warping. CVPR 2008 - [c83]Thomas J. Fuchs, Tilman Lange, Peter J. Wild, Holger Moch, Joachim M. Buhmann:
Weakly Supervised Cell Nuclei Detection and Segmentation on Tissue Microarrays of Renal Clear Cell Carcinoma. DAGM-Symposium 2008: 173-182 - [c82]Yvonne Moh, Wolfgang Einhäuser, Joachim M. Buhmann:
Automatic Detection of Learnability under Unreliable and Sparse User Feedback. DAGM-Symposium 2008: 224-233 - [c81]Yvonne Moh, Peter Orbanz, Joachim M. Buhmann:
Music preference learning with partial information. ICASSP 2008: 2021-2024 - [c80]Christian D. Sigg, Joachim M. Buhmann:
Expectation-maximization for sparse and non-negative PCA. ICML 2008: 960-967 - [c79]Yvonne Moh, Joachim M. Buhmann:
Kernel Expansion for Online Preference Tracking. ISMIR 2008: 167-172 - [c78]Thomas J. Fuchs, Peter J. Wild, Holger Moch, Joachim M. Buhmann:
Computational Pathology Analysis of Tissue Microarrays Predicts Survival of Renal Clear Cell Carcinoma Patients. MICCAI (2) 2008: 1-8 - [c77]Andreas P. Streich, Joachim M. Buhmann:
Classification of Multi-labeled Data: A Generative Approach. ECML/PKDD (2) 2008: 390-405 - 2007
- [j31]Franz F. Roos, Riko Jacob, Jonas Grossmann, Bernd Fischer, Joachim M. Buhmann, Wilhelm Gruissem, Sacha Baginsky, Peter Widmayer:
PepSplice: cache-efficient search algorithms for comprehensive identification of tandem mass spectra. Bioinform. 23(22): 3016-3023 (2007) - [j30]Bernd Fischer, Volker Roth, Joachim M. Buhmann:
Time-series alignment by non-negative multiple generalized canonical correlation analysis. BMC Bioinform. 8(S-10) (2007) - [j29]Thomas Zöller, Joachim M. Buhmann:
Robust Image Segmentation Using Resampling and Shape Constraints. IEEE Trans. Pattern Anal. Mach. Intell. 29(7): 1147-1164 (2007) - [c76]Björn Ommer, Joachim M. Buhmann:
Learning the Compositional Nature of Visual Objects. CVPR 2007 - [c75]Tilman Lange, Joachim M. Buhmann:
Regularized Data Fusion Improves Image Segmentation. DAGM-Symposium 2007: 234-243 - [c74]Tilman Lange, Joachim M. Buhmann:
Kernel-Based Grouping of Histogram Data. ECML 2007: 632-639 - [c73]Björn Ommer, Joachim M. Buhmann:
Compositional Object Recognition, Segmentation, and Tracking in Video. EMMCVPR 2007: 318-333 - [c72]Peter Orbanz, Samuel Braendle, Joachim M. Buhmann:
Bayesian Order-Adaptive Clustering for Video Segmentation. EMMCVPR 2007: 334-349 - [c71]Ludwig M. Busse, Peter Orbanz, Joachim M. Buhmann:
Cluster analysis of heterogeneous rank data. ICML 2007: 113-120 - [c70]Joachim M. Buhmann:
Complex Statistical Models for Object Recognition and Mass Spectrometry. PRIS 2007: 3-8 - [c69]Bernd Fischer, Volker Roth, Joachim M. Buhmann:
Time-Series Alignment by Non-negative Multiple Generalized Canonical Correlation Analysis. WILF 2007: 505-511 - 2006
- [j28]Julian Laub, Volker Roth, Joachim M. Buhmann, Klaus-Robert Müller:
On the information and representation of non-Euclidean pairwise data. Pattern Recognit. 39(10): 1815-1826 (2006) - [c68]Björn Ommer, Michael Sauter, Joachim M. Buhmann:
Learning Top-Down Grouping of Compositional Hierarchies for Recognition. CVPR Workshops 2006: 194 - [c67]Andrew Rabinovich, Serge J. Belongie, Tilman Lange, Joachim M. Buhmann:
Model Order Selection and Cue Combination for Image Segmentation. CVPR (1) 2006: 1130-1137 - [c66]Mikio L. Braun, Tilman Lange, Joachim M. Buhmann:
Model Selection in Kernel Methods Based on a Spectral Analysis of Label Information. DAGM-Symposium 2006: 344-353 - [c65]Hansruedi Peter, Bernd Fischer, Joachim M. Buhmann:
Probabilistic De Novo Peptide Sequencing with Doubly Charged Ions. DAGM-Symposium 2006: 424-433 - [c64]Peter Wey, Bernd Fischer, Herbert Bay, Joachim M. Buhmann:
Dense Stereo by Triangular Meshing and Cross Validation. DAGM-Symposium 2006: 708-717 - [c63]Björn Ommer, Joachim M. Buhmann:
Learning Compositional Categorization Models. ECCV (3) 2006: 316-329 - [c62]Peter Orbanz, Joachim M. Buhmann:
Smooth Image Segmentation by Nonparametric Bayesian Inference. ECCV (1) 2006: 444-457 - [c61]Isabelle Guyon, Amir Reza Saffari Azar Alamdari, Gideon Dror, Joachim M. Buhmann:
PerformancePrediction Challenge. IJCNN 2006: 1649-1656 - [c60]Bernd Fischer, Jonas Grossmann, Volker Roth, Wilhelm Gruissem, Sacha Baginsky, Joachim M. Buhmann:
Semi-supervised LC/MS alignment for differential proteomics. ISMB (Supplement of Bioinformatics) 2006: 132-140 - [c59]Mikio L. Braun, Joachim M. Buhmann, Klaus-Robert Müller:
Denoising and Dimension Reduction in Feature Space. NIPS 2006: 185-192 - 2005
- [j27]Joachim M. Buhmann, Tilman Lange, Ulrich Ramacher:
Image Segmentation by Networks of Spiking Neurons. Neural Comput. 17(5): 1010-1031 (2005) - [c58]Tilman Lange, Martin H. C. Law, Anil K. Jain, Joachim M. Buhmann:
Learning with Constrained and Unlabelled Data. CVPR (1) 2005: 731-738 - [c57]Björn Ommer, Joachim M. Buhmann:
Object Categorization by Compositional Graphical Models. EMMCVPR 2005: 235-250 - [c56]Peter Orbanz, Joachim M. Buhmann:
SAR images as mixtures of Gaussian mixtures. ICIP (2) 2005: 209-212 - [c55]Tilman Lange, Joachim M. Buhmann:
Combining partitions by probabilistic label aggregation. KDD 2005: 147-156 - [c54]Tilman Lange, Joachim M. Buhmann:
Fusion of Similarity Data in Clustering. NIPS 2005: 723-730 - 2004
- [j26]Tilman Lange, Volker Roth, Mikio L. Braun, Joachim M. Buhmann:
Stability-Based Validation of Clustering Solutions. Neural Comput. 16(6): 1299-1323 (2004) - [j25]Lothar Hermes, Joachim M. Buhmann:
Boundary-constrained agglomerative segmentation. IEEE Trans. Geosci. Remote. Sens. 42(9): 1984-1995 (2004) - [c53]Thomas Zöller, Joachim M. Buhmann:
Shape Constrained Image Segmentation by Parametric Distributional Clustering. CVPR (1) 2004: 386-393 - [c52]Anil K. Jain, Alexander P. Topchy, Martin H. C. Law, Joachim M. Buhmann:
Landscape of Clustering Algorithms. ICPR (1) 2004: 260-263 - [c51]Bernd Fischer, Volker Roth, Joachim M. Buhmann, Jonas Grossmann, Sacha Baginsky, Wilhelm Gruissem, Franz F. Roos, Peter Widmayer:
A Hidden Markov Model for de Novo Peptide Sequencing. NIPS 2004: 457-464 - [c50]Joachim M. Buhmann:
Clustering in Computer Vision and Data Analysis. PRIS 2004: 3 - 2003
- [j24]Bernd Fischer, Joachim M. Buhmann:
Path-Based Clustering for Grouping of Smooth Curves and Texture Segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 25(4): 513-518 (2003) - [j23]Bernd Fischer, Joachim M. Buhmann:
Bagging for Path-Based Clustering. IEEE Trans. Pattern Anal. Mach. Intell. 25(11): 1411-1415 (2003) - [j22]Volker Roth, Julian Laub, Motoaki Kawanabe, Joachim M. Buhmann:
Optimal Cluster Preserving Embedding of Nonmetric Proximity Data. IEEE Trans. Pattern Anal. Mach. Intell. 25(12): 1540-1551 (2003) - [j21]Lothar Hermes, Joachim M. Buhmann:
A minimum entropy approach to adaptive image polygonization. IEEE Trans. Image Process. 12(10): 1243-1258 (2003) - [c49]Wei-Jun Chen, Joachim M. Buhmann:
A New Distance Measure for Probabilistic Shape Modeling. DAGM-Symposium 2003: 507-514 - [c48]Lothar Hermes, Joachim M. Buhmann:
Semi-supervised Image Segmentation by Parametric Distributional Clustering. EMMCVPR 2003: 229-245 - [c47]Björn Ommer, Joachim M. Buhmann:
A Compositionally Architecture for Perceptual Feature Grouping. EMMCVPR 2003: 275-290 - [c46]Bernd Fischer, Volker Roth, Joachim M. Buhmann:
Clustering with the Connectivity Kernel. NIPS 2003: 89-96 - 2002
- [j20]Zvika Marx, Ido Dagan, Joachim M. Buhmann, Eli Shamir:
Coupled Clustering: A Method for Detecting Structural Correspondence. J. Mach. Learn. Res. 3: 747-780 (2002) - [c45]Volker Roth, Tilman Lange, Mikio L. Braun, Joachim M. Buhmann:
A Resampling Approach to Cluster Validation. COMPSTAT 2002: 123-128 - [c44]Bernd Fischer, Joachim M. Buhmann:
Data Resampling for Path Based Clustering. DAGM-Symposium 2002: 206-214 - [c43]Lothar Hermes, Thomas Zöller, Joachim M. Buhmann:
Parametric Distributional Clustering for Image Segmentation. ECCV (3) 2002: 577-591 - [c42]Volker Roth, Mikio L. Braun, Tilman Lange, Joachim M. Buhmann:
Stability-Based Model Order Selection in Clustering with Applications to Gene Expression Data. ICANN 2002: 607-612 - [c41]Thomas Zöller, Lothar Hermes, Joachim M. Buhmann:
Combined Color And Texture Segmentation by Parametric Distributional Clustering. ICPR (2) 2002: 627-630 - [c40]Tilman Lange, Mikio L. Braun, Volker Roth, Joachim M. Buhmann:
Stability-Based Model Selection. NIPS 2002: 617-624 - [c39]Volker Roth, Julian Laub, Joachim M. Buhmann, Klaus-Robert Müller:
Going Metric: Denoising Pairwise Data. NIPS 2002: 817-824 - 2001
- [j19]Yossi Rubner, Jan Puzicha, Carlo Tomasi, Joachim M. Buhmann:
Empirical Evaluation of Dissimilarity Measures for Color and Texture. Comput. Vis. Image Underst. 84(1): 25-43 (2001) - [c38]Lothar Hermes, Joachim M. Buhmann:
Contextual Classification by Entropy-Based Polygonization. CVPR (2) 2001: 442-447 - [c37]Markus Suing, Lothar Hermes, Joachim M. Buhmann:
A New Contour-Based Approach to Object Recognition for Assembly Line Robots. DAGM-Symposium 2001: 329-336 - [c36]Bernd Fischer, Thomas Zöller, Joachim M. Buhmann:
Path Based Pairwise Data Clustering with Application to Texture Segmentation. EMMCVPR 2001: 235-250 - [c35]Bjoern Stenger, Visvanathan Ramesh, Nikos Paragios, Frans Coetzee, Joachim M. Buhmann:
Topology Free Hidden Markov Models: Application to Background Modeling. ICCV 2001: 294-301 - [c34]Zvika Marx, Ido Dagan, Joachim M. Buhmann:
Coupled Clustering: a Method for Detecting Structural Correspondence. ICML 2001: 353-360 - [c33]Mikio L. Braun, Joachim M. Buhmann:
The Noisy Euclidean Traveling Salesman Problem and Learning. NIPS 2001: 351-358 - [i1]Zvika Marx, Ido Dagan, Joachim M. Buhmann:
Coupled Clustering: a Method for Detecting Structural Correspondence. CoRR cs.LG/0107032 (2001) - 2000
- [j18]Jan Puzicha, Thomas Hofmann, Joachim M. Buhmann:
A theory of proximity based clustering: structure detection by optimization. Pattern Recognit. 33(4): 617-634 (2000) - [j17]Hansjörg Klock, Joachim M. Buhmann:
Data visualization by multidimensional scaling: a deterministic annealing approach. Pattern Recognit. 33(4): 651-669 (2000) - [j16]Jan Puzicha, Marcus Held, Jens Ketterer, Joachim M. Buhmann, Dieter W. Fellner:
On spatial quantization of color images. IEEE Trans. Image Process. 9(4): 666-682 (2000) - [c32]Stefan Will, Lothar Hermes, Joachim M. Buhmann, Jan Puzicha:
On Learning Optimal Texture Edge Detectors. ICIP 2000: 877-880 - [c31]Joachim M. Buhmann, Thomas Zöller:
Active Learning for Hierarchical Pairwise Data Clustering. ICPR 2000: 2186-2189 - [c30]Lothar Hermes, Joachim M. Buhmann:
Feature Selection for Support Vector Machines. ICPR 2000: 2712-2715
1990 – 1999
- 1999
- [j15]Jan Puzicha, Joachim M. Buhmann:
Multiscale Annealing for Grouping and Unsupervised Texture Segmentation. Comput. Vis. Image Underst. 76(3): 213-230 (1999) - [j14]Jan Puzicha, Thomas Hofmann, Joachim M. Buhmann:
Histogram clustering for unsupervised segmentation and image retrieval. Pattern Recognit. Lett. 20(9): 899-909 (1999) - [c29]Jan Puzicha, Joachim M. Buhmann, Thomas Hofmann:
Histogram Clustering for Unsupervised Image Segmentation. CVPR 1999: 2602-2608 - [c28]Jan Puzicha, Yossi Rubner, Carlo Tomasi, Joachim M. Buhmann:
Empirical Evaluation of Dissimilarity Measures for Color and Texture. ICCV 1999: 1165-1172 - [c27]Joachim M. Buhmann, Marcus Held:
Model Selection in Clustering by Uniform Convergence Bounds. NIPS 1999: 216-222 - [e1]Wolfgang Förstner, Joachim M. Buhmann, Annett Faber, Petko Faber:
Mustererkennung 1999, 21. DAGM-Symposium, Bonn, 15.-17. September 1999, Proceedings. Informatik Aktuell, Springer 1999, ISBN 3-540-66381-9 [contents] - 1998
- [j13]Joachim M. Buhmann, Dieter W. Fellner, Marcus Held, Jens Ketterer, Jan Puzicha:
Dithered Color Quantization. Comput. Graph. Forum 17(3): 219-232 (1998) - [j12]Joachim M. Buhmann:
Knowledge-Discovery: Wie können wir Muster und Strukturen in Daten zuverlässig erkennen? Künstliche Intell. 12(1): 37 (1998) - [j11]Thomas Hofmann, Jan Puzicha, Joachim M. Buhmann:
Unsupervised Texture Segmentation in a Deterministic Annealing Framework. IEEE Trans. Pattern Anal. Mach. Intell. 20(8): 803-818 (1998) - [j10]Thomas Hofmann, Joachim M. Buhmann:
Competitive learning algorithms for robust vector quantization. IEEE Trans. Signal Process. 46(6): 1665-1675 (1998) - [c26]Jan Puzicha, Joachim M. Buhmann, Thomas Hofmann:
Discrete Mixture Models for Unsupervised Image Segmentation. DAGM-Symposium 1998: 135-142 - [c25]Jens Ketterer, Jan Puzicha, Marcus Held, Martin Fischer, Joachim M. Buhmann, Dieter W. Fellner:
On Spatial Quantization of Color Images. ECCV (1) 1998: 563-577 - [c24]Jan Puzicha, Joachim M. Buhmann:
Multiscale Annealing for Real-Time Unsupervised Texture Segmentation. ICCV 1998: 267-273 - [c23]Marcus Held, Jan Puzicha, Joachim M. Buhmann:
Visualizing Group Structure. NIPS 1998: 452-458 - [c22]Joachim M. Buhmann, Jan Puzicha:
Unsupervised Learning for Robust Texture Segmentation. Theoretical Foundations of Computer Vision 1998: 195-209 - [p1]Joachim M. Buhmann:
Stochastic Algorithms for Exploratory Data Analysis: Data Clustering and Data Visualization. Learning in Graphical Models 1998: 405-419 - 1997
- [j9]Thomas Hofmann, Joachim M. Buhmann:
Pairwise Data Clustering by Deterministic Annealing. IEEE Trans. Pattern Anal. Mach. Intell. 19(1): 1-14 (1997) - [j8]Thomas Hofmann, Joachim M. Buhmann:
Correction to "Pairwise Data Clustering by Deterministic Annealing". IEEE Trans. Pattern Anal. Mach. Intell. 19(2): 192 (1997) - [c21]Jan Puzicha, Thomas Hofmann, Joachim M. Buhmann:
Non-parametric Similarity Measures for Unsupervised Texture Segmentation and Image Retrieval. CVPR 1997: 267-272 - [c20]Thomas Hofmann, Jan Puzicha, Joachim M. Buhmann:
Deterministic Annealing for Unsupervised Texture Segmentation. EMMCVPR 1997: 213-228 - [c19]Hansjörg Klock, Joachim M. Buhmann:
Multidimensional Scaling by Deterministic Annealing. EMMCVPR 1997: 245-260 - [c18]Joachim M. Buhmann, Thomas Hofmann:
Robust vector quantization by competitive learning. ICASSP 1997: 139-142 - [c17]Thomas Hofmann, Jan Puzicha, Joachim M. Buhmann:
An Optimization Approach to Unsupervised Hierarchical Texture Segmentation. ICIP (3) 1997: 213-216 - [c16]Andreas Polzer, Hansjörg Klock, Joachim M. Buhmann:
Video coding by region-based motion compensation and spatio-temporal wavelet transform. ICIP (3) 1997: 436-439 - [c15]Hansjörg Klock, Andreas Polzer, Joachim M. Buhmann:
Region-Based Motion Compensated 3D-Wavelet Transform Coding of Video. ICIP (2) 1997: 776-779 - [c14]Marcus Held, Joachim M. Buhmann:
Unsupervised On-line Learning of Decision Trees for Hierarchical Data Analysis. NIPS 1997: 514-520 - [c13]Thomas Hofmann, Joachim M. Buhmann:
Active Data Clustering. NIPS 1997: 528-534 - 1996
- [c12]Thomas Hofmann, Joachim M. Buhmann:
An Annealed "Neural Gas" Network for Robust Vector Quantization. ICANN 1996: 151-156 - [c11]Thomas Hofmann, Jan Puzicha, Joachim M. Buhmann:
Unsupervised segmentation of textured images by pairwise data clustering. ICIP (3) 1996: 137-140 - [c10]Thorsten Fröhlinghaus, Joachim M. Buhmann:
Regularizing phase-based stereo. ICPR 1996: 451-455 - [c9]Thomas Hofmann, Joachim M. Buhmann:
Inferring Hierarchical Clustering Structures by Deterministic Annealing. KDD 1996: 363-366 - 1995
- [j7]Joachim M. Buhmann, Wolfram Burgard, Armin B. Cremers, Dieter Fox, Thomas Hofmann, Frank E. Schneider, Jiannis Strikos, Sebastian Thrun:
The Mobile Robot RHINO. AI Mag. 16(2): 31-38 (1995) - [j6]Armin B. Cremers, Joachim M. Buhmann, Sebastian Thrun:
Komplexe lernende Systeme: der mobile Roboter RHINO. Künstliche Intell. 9(2): 48-49 (1995) - [c8]Joachim M. Buhmann, Wolfram Burgard, Armin B. Cremers, Dieter Fox, Thomas Hofmann, Frank E. Schneider, Jiannis Strikos, Sebastian Thrun:
The Mobile Robot Rhino. SNN Symposium on Neural Networks 1995: 129-139 - 1994
- [c7]Joachim M. Buhmann, Thomas Hofmann:
A maximum entropy approach to pairwise data clustering. ICPR (2) 1994: 207-212 - [c6]Thomas Hofmann, Joachim M. Buhmann:
Multidimensional Scaling and Data Clustering. NIPS 1994: 459-466 - 1993
- [j5]Joachim M. Buhmann, Hans Kühnel:
Complexity Optimized Data Clustering by Competitive Neural Networks. Neural Comput. 5(1): 75-88 (1993) - [j4]Martin Lades, Jan C. Vorbrüggen, Joachim M. Buhmann, Jörg Lange, Christoph von der Malsburg, Rolf P. Würtz, Wolfgang Konen:
Distortion Invariant Object Recognition in the Dynamic Link Architecture. IEEE Trans. Computers 42(3): 300-311 (1993) - [j3]Joachim M. Buhmann, Hans Kühnel:
Vector quantization with complexity costs. IEEE Trans. Inf. Theory 39(4): 1133-1145 (1993) - [c5]Joachim M. Buhmann, Thomas Hofmann:
Central and Pairwise Data Clustering by Competitive Neural Networks. NIPS 1993: 104-111 - [c4]Joachim M. Buhmann, Martin Lades, Frank H. Eeckman:
Illumination-Invariant Face Recognition with a Contrast Sensitive Silicon Retina. NIPS 1993: 769-776 - 1992
- [j2]Christoph von der Malsburg, Joachim M. Buhmann:
Sensory segmentation with coupled neural oscillators. Biol. Cybern. 67(3): 233-242 (1992) - [c3]Joachim M. Buhmann, Hans Kühnel:
Complexity Optimized Vector Quantization: A Neutral Network Approach. Data Compression Conference 1992: 12-21 - 1990
- [j1]DeLiang Wang, Joachim M. Buhmann, Christoph von der Malsburg:
Pattern Segmentation in Associative Memory. Neural Comput. 2(1): 94-106 (1990) - [c2]Joachim M. Buhmann, Martin Lades, Christoph von der Malsburg:
Size and distortion invariant object recognition by hierarchical graph matching. IJCNN 1990: 411-416
1980 – 1989
- 1988
- [b1]Joachim M. Buhmann:
Neuronale Netzwerke als assoziative Speicher und als Systeme zur Mustererkennung. Technical University Munich, Germany, 1988, pp. 1-148 - [c1]Joachim M. Buhmann, Klaus Schulten:
Invariant pattern recognition by means of fast synaptic plasticity. ICNN 1988: 125-132
Coauthor Index
Ðorðe Miladinovic
aka: Djordje Miladinovic
aka: Djordje Miladinovic
Peter J. Schüffler
aka: Peter Schueffler
aka: Peter Schueffler
Klaas E. Stephan
aka: Klaas Enno Stephan
aka: Klaas Enno Stephan
Frans Vos
aka: Franciscus M. Vos
aka: Franciscus M. Vos
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