| Neural relational inference for interacting systems T Kipf, E Fetaya, KC Wang, M Welling, R Zemel International Conference on Machine Learning, 2018 | 990 | 2018 |
| Personalized federated learning using hypernetworks A Shamsian, A Navon, E Fetaya, G Chechik International Conference on Machine Learning, 9489-9502, 2021 | 319 | 2021 |
| On the Universality of Invariant Networks H Maron, E Fetaya, N Segol, Y Lipman International Conference on Machine Learning, 2019 | 256 | 2019 |
| Incremental few-shot learning with attention attractor networks M Ren, R Liao, E Fetaya, R Zemel Advances in neural information processing systems 32, 2019 | 206 | 2019 |
| Human pose estimation using deep consensus voting I Lifshitz, E Fetaya, S Ullman Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The …, 2016 | 206 | 2016 |
| StixelNet: A Deep Convolutional Network for Obstacle Detection and Road Segmentation. D Levi, N Garnett, E Fetaya BMVC, 109.1-109.12, 2015 | 195 | 2015 |
| Atoms of recognition in human and computer vision S Ullman, L Assif, E Fetaya, D Harari Proceedings of the National Academy of Sciences 113 (10), 2744-2749, 2016 | 190 | 2016 |
| Learning discrete weights using the local reparameterization trick O Shayer, D Levi, E Fetaya International Conference on Representation Learning, 2017 | 148 | 2017 |
| Evaluating and calibrating uncertainty prediction in regression tasks D Levi, L Gispan, N Giladi, E Fetaya Sensors 22 (15), 5540, 2022 | 135 | 2022 |
| Reviving and improving recurrent back-propagation R Liao, Y Xiong, E Fetaya, L Zhang, KJ Yoon, X Pitkow, R Urtasun, ... International Conference on Machine Learning, 3082-3091, 2018 | 135 | 2018 |
| Learning the pareto front with hypernetworks A Navon, A Shamsian, G Chechik, E Fetaya arXiv preprint arXiv:2010.04104, 2020 | 130 | 2020 |
| From local structures to size generalization in graph neural networks G Yehudai, E Fetaya, E Meirom, G Chechik, H Maron International Conference on Machine Learning, 11975-11986, 2021 | 126 | 2021 |
| On learning sets of symmetric elements H Maron, O Litany, G Chechik, E Fetaya International conference on machine learning, 6734-6744, 2020 | 123 | 2020 |
| Inference in probabilistic graphical models by graph neural networks KJ Yoon, R Liao, Y Xiong, L Zhang, E Fetaya, R Urtasun, R Zemel, ... 2019 53rd Asilomar Conference on Signals, Systems, and Computers, 868-875, 2019 | 114 | 2019 |
| Multi-task learning as a bargaining game A Navon, A Shamsian, I Achituve, H Maron, K Kawaguchi, G Chechik, ... arXiv preprint arXiv:2202.01017, 2022 | 106 | 2022 |
| Real-time category-based and general obstacle detection for autonomous driving N Garnett, S Silberstein, S Oron, E Fetaya, U Verner, A Ayash, V Goldner, ... Proceedings of the IEEE international conference on computer vision …, 2017 | 102 | 2017 |
| Personalized federated learning with gaussian processes I Achituve, A Shamsian, A Navon, G Chechik, E Fetaya Advances in Neural Information Processing Systems 34, 8392-8406, 2021 | 101 | 2021 |
| Auxiliary learning by implicit differentiation A Navon, I Achituve, H Maron, G Chechik, E Fetaya arXiv preprint arXiv:2007.02693, 2020 | 59 | 2020 |
| Understanding the Limitations of Conditional Generative Models E Fetaya, JH Jacobsen, R Zemel arXiv preprint arXiv:1906.01171, 2019 | 58* | 2019 |
| Unsupervised ensemble learning with dependent classifiers A Jaffe, E Fetaya, B Nadler, T Jiang, Y Kluger Artificial Intelligence and Statistics, 351-360, 2016 | 53 | 2016 |
Richard ZemelProfessor of Computer Science, University of TorontoVerified email at cs.toronto.edu
