| Learning to reweight examples for robust deep learning M Ren, W Zeng, B Yang, R Urtasun International Conference on Machine Learning, 4331-4340, 2018 | 1367 | 2018 |
| Meta-learning for semi-supervised few-shot classification M Ren, E Triantafillou, S Ravi, J Snell, K Swersky, JB Tenenbaum, ... International Conference on Learning Representations, 2018 | 1365 | 2018 |
| Exploring models and data for image question answering M Ren, R Kiros, R Zemel Advances in Neural Information Processing Systems 28, 2953-2961, 2015 | 962* | 2015 |
| The reversible residual network: Backpropagation without storing activations AN Gomez, M Ren, R Urtasun, RB Grosse Advances in Neural Information Processing Systems 30, 2211-2221, 2017 | 497 | 2017 |
| End-to-end instance segmentation with recurrent attention M Ren, RS Zemel Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017 | 386 | 2017 |
| Graph hypernetworks for neural architecture search C Zhang, M Ren, R Urtasun International Conference on Learning Representations, 2019 | 251 | 2019 |
| SBNet: Sparse blocks network for fast inference M Ren, A Pokrovsky, B Yang, R Urtasun Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 215 | 2018 |
| Incremental few-shot learning with attention attractor networks M Ren, R Liao, E Fetaya, RS Zemel Advances in Neural Information Processing Systems 32, 5275-5285, 2019 | 186 | 2019 |
| Physically realizable adversarial examples for LiDAR object detection J Tu, M Ren, S Manivasagam, M Liang, B Yang, R Du, F Cheng, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 177 | 2020 |
| Perceive, predict, and plan: Safe motion planning through interpretable semantic representations A Sadat, S Casas, M Ren, X Wu, P Dhawan, R Urtasun European Conference on Computer Vision, 414-430, 2020 | 129 | 2020 |
| Understanding short-horizon bias in stochastic meta-optimization Y Wu, M Ren, R Liao, R Grosse International Conference on Learning Representations, 2018 | 124 | 2018 |
| Normalizing the normalizers: Comparing and extending network normalization schemes M Ren, R Liao, R Urtasun, FH Sinz, RS Zemel International Conference on Learning Representations, 2017 | 103 | 2017 |
| End-to-end contextual perception and prediction with interaction transformer LL Li, B Yang, M Liang, W Zeng, M Ren, S Segal, R Urtasun IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020 | 96 | 2020 |
| AdvSim: Generating safety-critical scenarios for self-driving vehicles J Wang, A Pun, J Tu, S Manivasagam, A Sadat, S Casas, M Ren, ... arXiv preprint arXiv:2101.06549, 2021 | 88 | 2021 |
| Identifying unknown instances for autonomous driving K Wong, S Wang, M Ren, M Liang, R Urtasun Conference on Robot Learning, 384-393, 2020 | 85 | 2020 |
| Jointly learnable behavior and trajectory planning for self-driving vehicles A Sadat, M Ren, A Pokrovsky, YC Lin, E Yumer, R Urtasun IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019 | 78 | 2019 |
| LoCo: Local contrastive representation learning Y Xiong, M Ren, R Urtasun Advances in Neural Information Processing Systems 33, 2020 | 50 | 2020 |
| Exploring adversarial robustness of multi-sensor perception systems in self driving J Tu, H Li, X Yan, M Ren, Y Chen, M Liang, E Bitar, E Yumer, R Urtasun arXiv preprint arXiv:2101.06784, 2021 | 47 | 2021 |
| SceneGen: Learning to generate realistic traffic scenes S Tan, K Wong, S Wang, S Manivasagam, M Ren, R Urtasun arXiv preprint arXiv:2101.06541, 2021 | 47 | 2021 |
| Multi-agent routing value iteration network Q Sykora, M Ren, R Urtasun International Conference on Machine Learning, 9300-9310, 2020 | 40 | 2020 |
Raquel UrtasunFounder & CEO Waabi, Professor, University of Toronto.Verified email at cs.toronto.edu
