| Non-negative tensor factorization with applications to statistics and computer vision A Shashua, T Hazan Proceedings of the 22nd international conference on Machine learning, 792-799, 2005 | 616 | 2005 |
| Sparse image coding using a 3D non-negative tensor factorization T Hazan, S Polak, A Shashua Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 1 …, 2005 | 231 | 2005 |
| Perturbations, optimization, and statistics T Hazan, G Papandreou, D Tarlow MIT Press, 2016 | 225* | 2016 |
| Direct loss minimization for structured prediction T Hazan, J Keshet, D McAllester Advances in neural information processing systems 23, 2010 | 166 | 2010 |
| Multi-way clustering using super-symmetric non-negative tensor factorization A Shashua, R Zass, T Hazan ECCV (4) 3954, 595-608, 2006 | 164 | 2006 |
| Continuous markov random fields for robust stereo estimation K Yamaguchi, T Hazan, D McAllester, R Urtasun Computer Vision–ECCV 2012: 12th European Conference on Computer Vision …, 2012 | 160 | 2012 |
| Efficient structured prediction for 3d indoor scene understanding AG Schwing, T Hazan, M Pollefeys, R Urtasun 2012 IEEE conference on computer vision and pattern recognition, 2815-2822, 2012 | 150 | 2012 |
| Norm-product belief propagation: Primal-dual message-passing for approximate inference T Hazan, A Shashua IEEE Transactions on Information Theory 56 (12), 6294-6316, 2010 | 143 | 2010 |
| Factor graph attention I Schwartz, S Yu, T Hazan, AG Schwing Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 101 | 2019 |
| On the partition function and random maximum a-posteriori perturbations T Hazan, T Jaakkola Proceedings of the 29th International Conference on Machine Learning, 2012 | 100 | 2012 |
| A primal-dual message-passing algorithm for approximated large scale structured prediction T Hazan, R Urtasun Advances in neural information processing systems 23, 2010 | 98 | 2010 |
| High-order attention models for visual question answering I Schwartz, A Schwing, T Hazan Advances in Neural Information Processing Systems 30, 2017 | 97 | 2017 |
| Distributed message passing for large scale graphical models A Schwing, T Hazan, M Pollefeys, R Urtasun CVPR 2011, 1833-1840, 2011 | 97 | 2011 |
| Modeling appearances with low-rank svm L Wolf, H Jhuang, T Hazan 2007 IEEE Conference on Computer Vision and Pattern Recognition, 1-6, 2007 | 90 | 2007 |
| Convergent message-passing algorithms for inference over general graphs with convex free energies T Hazan, A Shashua The 24th Conference on Uncertainty in Artificial Intelligence, 2008 | 84* | 2008 |
| Steps toward deep kernel methods from infinite neural networks T Hazan, T Jaakkola arXiv preprint arXiv:1508.05133, 2015 | 83 | 2015 |
| Efficient structured prediction with latent variables for general graphical models A Schwing, T Hazan, M Pollefeys, R Urtasun Proceedings of the 29th International Conference on Machine Learning, 2012 | 79 | 2012 |
| On sampling from the gibbs distribution with random maximum a-posteriori perturbations T Hazan, S Maji, T Jaakkola Advances in Neural Information Processing Systems 26, 2013 | 76 | 2013 |
| Pac-bayesian approach for minimization of phoneme error rate J Keshet, D McAllester, T Hazan 2011 IEEE International Conference on Acoustics, Speech and Signal …, 2011 | 55 | 2011 |
| Psychological forest: Predicting human behavior O Plonsky, I Erev, T Hazan, M Tennenholtz Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017 | 54 | 2017 |
