| On feature combination for multiclass object classification P Gehler, S Nowozin 2009 IEEE 12th International Conference on Computer Vision, 221-228, 2009 | 946 | 2009 |
| f-gan: Training generative neural samplers using variational divergence minimization S Nowozin, B Cseke, R Tomioka Advances in neural information processing systems, 271-279, 2016 | 581 | 2016 |
| Optimization for machine learning S Sra, S Nowozin, SJ Wright Mit Press, 2012 | 425 | 2012 |
| Instructing people for training gestural interactive systems S Fothergill, H Mentis, P Kohli, S Nowozin Proceedings of the SIGCHI Conference on Human Factors in Computing Systems …, 2012 | 382 | 2012 |
| Structured learning and prediction in computer vision S Nowozin, CH Lampert Foundations and Trends® in Computer Graphics and Vision 6 (3–4), 185-365, 2011 | 331 | 2011 |
| A comparative study of modern inference techniques for discrete energy minimization problems J Kappes, B Andres, F Hamprecht, C Schnorr, S Nowozin, D Batra, S Kim, ... Proceedings of the IEEE conference on computer vision and pattern …, 2013 | 312 | 2013 |
| Adversarial variational bayes: Unifying variational autoencoders and generative adversarial networks L Mescheder, S Nowozin, A Geiger Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017 | 250 | 2017 |
| Discriminative subsequence mining for action classification S Nowozin, G Bakir, K Tsuda 2007 IEEE 11th International Conference on Computer Vision, 1-8, 2007 | 233 | 2007 |
| Oblivious multi-party machine learning on trusted processors O Ohrimenko, F Schuster, C Fournet, A Mehta, S Nowozin, K Vaswani, ... 25th {USENIX} Security Symposium ({USENIX} Security 16), 619-636, 2016 | 223 | 2016 |
| Deepcoder: Learning to write programs M Balog, AL Gaunt, M Brockschmidt, S Nowozin, D Tarlow arXiv preprint arXiv:1611.01989, 2016 | 209 | 2016 |
| Which training methods for GANs do actually converge? L Mescheder, A Geiger, S Nowozin arXiv preprint arXiv:1801.04406, 2018 | 200 | 2018 |
| Pixeldefend: Leveraging generative models to understand and defend against adversarial examples Y Song, T Kim, S Nowozin, S Ermon, N Kushman arXiv preprint arXiv:1710.10766, 2017 | 196 | 2017 |
| Stabilizing training of generative adversarial networks through regularization K Roth, A Lucchi, S Nowozin, T Hofmann Advances in neural information processing systems, 2018-2028, 2017 | 141 | 2017 |
| Dsac-differentiable ransac for camera localization E Brachmann, A Krull, S Nowozin, J Shotton, F Michel, S Gumhold, ... Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017 | 141 | 2017 |
| Decision tree fields S Nowozin, C Rother, S Bagon, T Sharp, B Yao, P Kohli 2011 International Conference on Computer Vision, 1668-1675, 2011 | 131 | 2011 |
| gBoost: a mathematical programming approach to graph classification and regression H Saigo, S Nowozin, T Kadowaki, T Kudo, K Tsuda Machine Learning 75 (1), 69-89, 2009 | 125 | 2009 |
| Weighted substructure mining for image analysis S Nowozin, K Tsuda, T Uno, T Kudo, G BakIr 2007 IEEE Conference on Computer Vision and Pattern Recognition, 1-8, 2007 | 123 | 2007 |
| Higher-order correlation clustering for image segmentation S Kim, S Nowozin, P Kohli, CD Yoo Advances in neural information processing systems, 1530-1538, 2011 | 122 | 2011 |
| Global connectivity potentials for random field models S Nowozin, CH Lampert 2009 IEEE Conference on Computer Vision and Pattern Recognition, 818-825, 2009 | 115 | 2009 |
| Loss-specific training of non-parametric image restoration models: A new state of the art J Jancsary, S Nowozin, C Rother European Conference on Computer Vision, 112-125, 2012 | 110 | 2012 |
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