| Inferring subnetworks from perturbed expression profiles D Pe’er, A Regev, G Elidan, N Friedman Bioinformatics 17 (suppl_1), S215-S224, 2001 | 762 | 2001 |
| Multi-class segmentation with relative location prior S Gould, J Rodgers, D Cohen, G Elidan, D Koller International journal of computer vision 80, 300-316, 2008 | 527 | 2008 |
| Residual belief propagation: Informed scheduling for asynchronous message passing G Elidan, I McGraw, D Koller arXiv preprint arXiv:1206.6837, 2012 | 379 | 2012 |
| Modeling dependencies in protein-DNA binding sites Y Barash, G Elidan, N Friedman, T Kaplan Proceedings of the seventh annual international conference on Research in …, 2003 | 319 | 2003 |
| Explaining in style: training a GAN to explain a classifier in stylespace O Lang, Y Gandelsman, M Yarom, Y Wald, G Elidan, A Hassidim, ... Proceedings of the IEEE/CVF International Conference on Computer Vision, 693-702, 2021 | 173 | 2021 |
| Markov random field based automatic image alignment for electron tomography F Amat, F Moussavi, LR Comolli, G Elidan, KH Downing, M Horowitz Journal of structural biology 161 (3), 260-275, 2008 | 170 | 2008 |
| Discovering hidden variables: A structure-based approach G Elidan, N Lotner, N Friedman, D Koller Advances in Neural Information Processing Systems 13, 2000 | 170 | 2000 |
| Copula bayesian networks G Elidan Advances in neural information processing systems 23, 2010 | 159 | 2010 |
| Max-margin classification of data with absent features G Chechik, G Heitz, G Elidan, P Abbeel, D Koller The Journal of Machine Learning Research 9, 1-21, 2008 | 157 | 2008 |
| Flood forecasting with machine learning models in an operational framework S Nevo, E Morin, A Gerzi Rosenthal, A Metzger, C Barshai, D Weitzner, ... Hydrology and Earth System Sciences 26 (15), 4013-4032, 2022 | 148 | 2022 |
| Scalable learning of non-decomposable objectives E Eban, M Schain, A Mackey, A Gordon, R Rifkin, G Elidan Artificial intelligence and statistics, 832-840, 2017 | 141 | 2017 |
| Learning Hidden Variable Networks: The Information Bottleneck Approach. G Elidan, N Friedman, DM Chickering Journal of Machine Learning Research 6 (1), 2005 | 138 | 2005 |
| Net-dnf: Effective deep modeling of tabular data L Katzir, G Elidan, R El-Yaniv International conference on learning representations, 2020 | 113 | 2020 |
| Data perturbation for escaping local maxima in learning G Elidan, M Ninio, N Friedman, D Schuurmans AAAI/IAAI 132, 139, 2002 | 108 | 2002 |
| Learning bounded treewidth Bayesian networks G Elidan, S Gould Advances in neural information processing systems 21, 2008 | 105 | 2008 |
| Using combinatorial optimization within max-product belief propagation D Tarlow, G Elidan, D Koller, JC Duchi Advances in neural information processing systems 19, 2006 | 100 | 2006 |
| Copulas in machine learning G Elidan Copulae in Mathematical and Quantitative Finance: Proceedings of the …, 2013 | 96 | 2013 |
| Learning the dimensionality of hidden variables G Elidan, N Friedman arXiv preprint arXiv:1301.2269, 2013 | 92 | 2013 |
| Towards an integrated protein–protein interaction network: A relational markov network approach A Jaimovich, G Elidan, H Margalit, N Friedman Journal of Computational Biology 13 (2), 145-164, 2006 | 86 | 2006 |
| Factually consistent summarization via reinforcement learning with textual entailment feedback P Roit, J Ferret, L Shani, R Aharoni, G Cideron, R Dadashi, M Geist, ... arXiv preprint arXiv:2306.00186, 2023 | 79 | 2023 |
Nir FriedmanProfessor of Computer Science and Medicine, Hebrew UniversityE-mail megerősítve itt: mail.huji.ac.il
