| Neural relational inference for interacting systems T Kipf, E Fetaya, KC Wang, M Welling, R Zemel International Conference on Machine Learning, 2018 | 503 | 2018 |
| Human pose estimation using deep consensus voting I Lifshitz, E Fetaya, S Ullman European Conference on Computer Vision, 246-260, 2016 | 167 | 2016 |
| StixelNet: A Deep Convolutional Network for Obstacle Detection and Road Segmentation. D Levi, N Garnett, E Fetaya BMVC, 109.1-109.12, 2015 | 146 | 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 | 140 | 2016 |
| Incremental few-shot learning with attention attractor networks M Ren, R Liao, E Fetaya, R Zemel Advances in Neural Information Processing Systems 32, 2019 | 127 | 2019 |
| On the Universality of Invariant Networks H Maron, E Fetaya, N Segol, Y Lipman International Conference on Machine Learning, 2019 | 118 | 2019 |
| Learning discrete weights using the local reparameterization trick O Shayer, D Levi, E Fetaya International Conference on Representation Learning, 2017 | 88 | 2017 |
| 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 | 67 | 2019 |
| 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 | 64 | 2018 |
| On learning sets of symmetric elements H Maron, O Litany, G Chechik, E Fetaya International Conference on Machine Learning, 6734-6744, 2020 | 54 | 2020 |
| 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 | 47 | 2017 |
| Unsupervised ensemble learning with dependent classifiers A Jaffe, E Fetaya, B Nadler, T Jiang, Y Kluger Artificial Intelligence and Statistics, 351-360, 2016 | 42 | 2016 |
| Evaluating and calibrating uncertainty prediction in regression tasks D Levi, L Gispan, N Giladi, E Fetaya arXiv preprint arXiv:1905.11659, 2019 | 36 | 2019 |
| Personalized federated learning using hypernetworks A Shamsian, A Navon, E Fetaya, G Chechik International Conference on Machine Learning, 9489-9502, 2021 | 33 | 2021 |
| Understanding the Limitations of Conditional Generative Models E Fetaya, JH Jacobsen, R Zemel arXiv preprint arXiv:1906.01171, 2019 | 29* | 2019 |
| Learning the pareto front with hypernetworks A Navon, A Shamsian, G Chechik, E Fetaya arXiv preprint arXiv:2010.04104, 2020 | 27 | 2020 |
| Neural guided constraint logic programming for program synthesis L Zhang, G Rosenblatt, E Fetaya, R Liao, W Byrd, M Might, R Urtasun, ... Advances in Neural Information Processing Systems 31, 2018 | 27 | 2018 |
| 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 | 24 | 2021 |
| Learning local invariant mahalanobis distances E Fetaya, S Ullman International conference on machine learning, 162-168, 2015 | 23 | 2015 |
| Unified deep convolutional neural net for free-space estimation, object detection and object pose estimation D Levi, N Garnett, E Fetaya, S Oron US Patent 10,474,908, 2019 | 20 | 2019 |
Richard ZemelProfessor of Computer Science, University of Toronto在 cs.toronto.edu 的电子邮件经过验证
