| Loss-specific training of non-parametric image restoration models: A new state of the art J Jancsary, S Nowozin, C Rother Computer Vision–ECCV 2012: 12th European Conference on Computer Vision …, 2012 | 179 | 2012 |
| Discriminative non-blind deblurring U Schmidt, C Rother, S Nowozin, J Jancsary, S Roth Proceedings of the IEEE conference on computer vision and pattern …, 2013 | 171 | 2013 |
| Joint demosaicing and denoising via learned nonparametric random fields D Khashabi, S Nowozin, J Jancsary, AW Fitzgibbon IEEE Transactions on Image Processing 23 (12), 4968-4981, 2014 | 128 | 2014 |
| Cascades of regression tree fields for image restoration U Schmidt, J Jancsary, S Nowozin, S Roth, C Rother IEEE transactions on pattern analysis and machine intelligence 38 (4), 677-689, 2015 | 111 | 2015 |
| Regression Tree Fields—An efficient, non-parametric approach to image labeling problems J Jancsary, S Nowozin, T Sharp, C Rother 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2376-2383, 2012 | 98 | 2012 |
| Advanced structured prediction S Nowozin, PV Gehler, J Jancsary, CH Lampert MIT Press, 2014 | 39 | 2014 |
| Interleaved regression tree field cascades for blind image deconvolution K Schelten, S Nowozin, J Jancsary, C Rother, S Roth 2015 IEEE Winter Conference on Applications of Computer Vision, 494-501, 2015 | 32 | 2015 |
| Blind image deblurring with cascade architecture K Schelten, RSB Nowozin, J Jancsary, CCE Rother US Patent 9,430,817, 2016 | 27 | 2016 |
| Convergent Decomposition Solvers for Tree-reweighted Free Energies J Jancsary, G Matz Journal of Machine Learning Research - Proceedings Track 15, 388-398, 2011 | 25 | 2011 |
| Image demosaicing RSB Nowozin, D Khashabi, JM Jancsary, BJ Lindbloom, AW Fitzgibbon US Patent 9,344,690, 2016 | 22 | 2016 |
| Image restoration cascade J Jancsary, RSB Nowozin, CCE Rother US Patent 9,396,523, 2016 | 19 | 2016 |
| Revealing the structure of medical dictations with conditional random fields J Jancsary, J Matiasek, H Trost Proceedings of the 2008 conference on empirical methods in natural language …, 2008 | 18 | 2008 |
| Learning convex QP relaxations for structured prediction J Jancsary, S Nowozin, C Rother International Conference on Machine Learning, 915-923, 2013 | 17 | 2013 |
| Towards context-aware personalization and a broad perspective on the semantics of news articles J Jancsary, F Neubarth, H Trost Proceedings of the fourth ACM conference on Recommender systems, 289-292, 2010 | 17 | 2010 |
| Image deblurring J Jancsary, UJ Schmidt, RSB Nowozin, CCE Rother US Patent App. 13/862,415, 2014 | 12 | 2014 |
| Perturb-and-map random fields: Reducing random sampling to optimization, with applications in computer vision G Papandreou, A Yuille | 10 | 2014 |
| The power of lp relaxation for map inference S Živný, T Werner, D Průša | 8 | 2014 |
| Semantic and phonetic automatic reconstruction of medical dictations S Petrik, C Drexel, L Fessler, J Jancsary, A Klein, G Kubin, J Matiasek, ... Computer Speech & Language 25 (2), 363-385, 2011 | 7 | 2011 |
| Smoothed coordinate descent for map inference O Meshi, T Jaakkola, A Globerson | 6 | 2014 |
| Generalized sequential tree-reweighted message passing T Schoenemann, V Kolmogorov | 6 | 2014 |