| Large scale online learning of image similarity through ranking G Chechik, V Sharma, U Shalit, S Bengio The Journal of Machine Learning Research 11, 1109-1135, 2010 | 963 | 2010 |
| Openimages: A public dataset for large-scale multi-label and multi-class image classification I Krasin, T Duerig, N Alldrin, V Ferrari, S Abu-El-Haija, A Kuznetsova, ... Dataset available from https://github. com/openimages 2 (3), 2-3, 2017 | 737 | 2017 |
| Learning from noisy large-scale datasets with minimal supervision A Veit, N Alldrin, G Chechik, I Krasin, A Gupta, S Belongie Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017 | 509 | 2017 |
| An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion R Gal, Y Alaluf, Y Atzmon, O Patashnik, AH Bermano, G Chechik, ... arXiv preprint arXiv:2208.01618, 2022 | 441 | 2022 |
| Euclidean embedding of co-occurrence data A Globerson, G Chechik, F Pereira, N Tishby Advances in neural information processing systems 17, 497-504, 2004 | 355 | 2004 |
| Information bottleneck for Gaussian variables G Chechik, A Globerson, N Tishby, Y Weiss Advances in Neural Information Processing Systems 16, 2003 | 332 | 2003 |
| Functional organization of the S. cerevisiae phosphorylation network D Fiedler, H Braberg, M Mehta, G Chechik, G Cagney, P Mukherjee, ... Cell 136 (5), 952-963, 2009 | 308 | 2009 |
| Reduction of information redundancy in the ascending auditory pathway G Chechik, MJ Anderson, O Bar-Yosef, ED Young, N Tishby, I Nelken Neuron 51 (3), 359-368, 2006 | 271 | 2006 |
| Synaptic pruning in development: A computational account G Chechik, I Meilijson, E Ruppin Neural Computation 10 (7), 1759-1777, 1998 | 249 | 1998 |
| Neuronal regulation: A mechanism for synaptic pruning during brain maturation G Chechik, I Meilijson, E Ruppin Neural computation 11 (8), 2061-2080, 1999 | 247 | 1999 |
| StyleGAN-NADA: CLIP-guided domain adaptation of image generators R Gal, O Patashnik, H Maron, AH Bermano, G Chechik, D Cohen-Or ACM Transactions on Graphics (TOG) 41 (4), 1-13, 2022 | 241 | 2022 |
| Activity motifs reveal principles of timing in transcriptional control of the yeast metabolic network G Chechik, E Oh, O Rando, J Weissman, A Regev, D Koller Nature biotechnology 26 (11), 1251, 2008 | 198 | 2008 |
| Personalized federated learning using hypernetworks A Shamsian, A Navon, E Fetaya, G Chechik International Conference on Machine Learning, 9489-9502, 2021 | 193 | 2021 |
| Encoding stimulus information by spike numbers and mean response time in primary auditory cortex I Nelken, G Chechik, TD Mrsic-Flogel, AJ King, JWH Schnupp Journal of computational neuroscience 19 (2), 199-221, 2005 | 188 | 2005 |
| 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 | 176* | 2008 |
| A unifying principle underlying the extracellular field potential spectral responses in the human cortex E Podvalny, N Noy, M Harel, S Bickel, G Chechik, CE Schroeder, ... Journal of neurophysiology 114 (1), 505-519, 2015 | 170 | 2015 |
| Context-aware captions from context-agnostic supervision R Vedantam, S Bengio, K Murphy, D Parikh, G Chechik Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017 | 157 | 2017 |
| An online algorithm for large scale image similarity learning G Chechik, V Sharma, U Shalit, S Bengio Advances in Neural Information Processing Systems 21, 306-314, 2009 | 144 | 2009 |
| 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, 7211-7221, 2018 | 135 | 2018 |
| Self-Supervised Learning for Domain Adaptation on Point Clouds I Achituve, H Maron, G Chechik Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2021 | 129 | 2021 |
Samy BengioSenior Director, AI and Machine Learning Research, AppleVerified email at apple.com
