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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]