| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ... arXiv preprint arXiv:2312.11805, 2023 | 924 | 2023 |
| Metric learning by collapsing classes A Globerson, S Roweis Advances in neural information processing systems 18, 2005 | 904 | 2005 |
| Nightmare at test time: robust learning by feature deletion A Globerson, S Roweis Proceedings of the 23rd international conference on Machine learning, 353-360, 2006 | 452 | 2006 |
| Fixing max-product: Convergent message passing algorithms for MAP LP-relaxations A Globerson, T Jaakkola Advances in neural information processing systems 20, 2007 | 431 | 2007 |
| Tightening LP relaxations for MAP using message passing D Sontag, T Meltzer, A Globerson, TS Jaakkola, Y Weiss arXiv preprint arXiv:1206.3288, 2012 | 367 | 2012 |
| Euclidean embedding of co-occurrence data A Globerson, G Chechik, F Pereira, N Tishby Advances in neural information processing systems 17, 2004 | 349 | 2004 |
| Information bottleneck for Gaussian variables G Chechik, A Globerson, N Tishby, Y Weiss Advances in Neural Information Processing Systems 16, 2003 | 341 | 2003 |
| Globally optimal gradient descent for a convnet with gaussian inputs A Brutzkus, A Globerson International conference on machine learning, 605-614, 2017 | 323 | 2017 |
| Learning Bayesian network structure using LP relaxations T Jaakkola, D Sontag, A Globerson, M Meila Proceedings of the thirteenth international conference on artificial …, 2010 | 294 | 2010 |
| SGD learns over-parameterized networks that provably generalize on linearly separable data A Brutzkus, A Globerson, E Malach, S Shalev-Shwartz arXiv preprint arXiv:1710.10174, 2017 | 282 | 2017 |
| Introduction to dual decomposition for inference D Sontag, A Globerson, T Jaakkola | 249 | 2011 |
| Cross-lingual alignment of contextual word embeddings, with applications to zero-shot dependency parsing T Schuster, O Ram, R Barzilay, A Globerson arXiv preprint arXiv:1902.09492, 2019 | 216 | 2019 |
| Exponentiated gradient algorithms for conditional random fields and max-margin markov networks M Collins, A Globerson, T Koo, X Carreras Pérez, P Bartlett Journal of Machine Learning Research 9, 1775-1822, 2008 | 209 | 2008 |
| Structured prediction models via the matrix-tree theorem T Koo, A Globerson, X Carreras Pérez, M Collins Joint Conference on Empirical Methods in Natural Language Processing and …, 2007 | 177 | 2007 |
| Selective sharing for multilingual dependency parsing T Naseem, R Barzilay, A Globerson The Association for Computational Linguistics, 2012 | 176 | 2012 |
| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context M Reid, N Savinov, D Teplyashin, D Lepikhin, T Lillicrap, J Alayrac, ... arXiv preprint arXiv:2403.05530, 2024 | 154 | 2024 |
| Sufficient dimensionality reduction A Globerson, N Tishby Journal of Machine Learning Research 3 (Mar), 1307-1331, 2003 | 148 | 2003 |
| Mapping images to scene graphs with permutation-invariant structured prediction R Herzig, M Raboh, G Chechik, J Berant, A Globerson Advances in Neural Information Processing Systems 31, 2018 | 142 | 2018 |
| 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 | 140 | 2021 |
| Convergent message passing algorithms-a unifying view T Meltzer, A Globerson, Y Weiss arXiv preprint arXiv:1205.2625, 2012 | 140 | 2012 |
Gal ChechikNVIDIA, Bar Ilan University在 biu.ac.il 的电子邮件经过验证
