| Detail-revealing deep video super-resolution X Tao, H Gao, R Liao, J Wang, J Jia Proceedings of the IEEE International Conference on Computer Vision, 4472-4480, 2017 | 234 | 2017 |
| 3d graph neural networks for rgbd semantic segmentation X Qi, R Liao, J Jia, S Fidler, R Urtasun Proceedings of the IEEE International Conference on Computer Vision, 5199-5208, 2017 | 215 | 2017 |
| Video super-resolution via deep draft-ensemble learning R Liao, X Tao, R Li, Z Ma, J Jia Proceedings of the IEEE International Conference on Computer Vision, 531-539, 2015 | 171 | 2015 |
| Deep edge-aware filters L Xu, J Ren, Q Yan, R Liao, J Jia International Conference on Machine Learning, 1669-1678, 2015 | 159 | 2015 |
| Geonet: Geometric neural network for joint depth and surface normal estimation X Qi, R Liao, Z Liu, R Urtasun, J Jia Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 143 | 2018 |
| Learning Important Spatial Pooling Regions for Scene Classification D Lin, C Lu, R Liao, J Jia IEEE Computer Vision and Pattern Recognition, 2014 | 120 | 2014 |
| Upsnet: A unified panoptic segmentation network Y Xiong, R Liao, H Zhao, R Hu, M Bai, E Yumer, R Urtasun Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 115 | 2019 |
| Handling motion blur in multi-frame super-resolution Z Ma, R Liao, X Tao, L Xu, J Jia, E Wu Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2015 | 112 | 2015 |
| Learning to generate images with perceptual similarity metrics J Snell, K Ridgeway, R Liao, B Roads, MC Mozer, RS Zemel IEEE International Conference on Image Processing, 2017 | 101* | 2017 |
| Lanczosnet: Multi-scale deep graph convolutional networks R Liao, Z Zhao, R Urtasun, RS Zemel International Conference on Learning Representations, 2019 | 82 | 2019 |
| Nervenet: Learning structured policy with graph neural networks T Wang, R Liao, J Ba, S Fidler International Conference on Learning Representations, 2018 | 82 | 2018 |
| Situation recognition with graph neural networks R Li, M Tapaswi, R Liao, J Jia, R Urtasun, S Fidler Proceedings of the IEEE International Conference on Computer Vision, 4173-4182, 2017 | 77 | 2017 |
| Learning deep structured active contours end-to-end D Marcos, D Tuia, B Kellenberger, L Zhang, M Bai, R Liao, R Urtasun Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 73 | 2018 |
| Learning deep parsimonious representations R Liao, A Schwing, R Zemel, R Urtasun Advances in Neural Information Processing Systems, 5076-5084, 2016 | 71 | 2016 |
| Normalizing the normalizers: Comparing and extending network normalization schemes M Ren, R Liao, R Urtasun, FH Sinz, RS Zemel International Conference on Learning Representations, 2016 | 65 | 2016 |
| Efficient graph generation with graph recurrent attention networks R Liao, Y Li, Y Song, S Wang, C Nash, WL Hamilton, D Duvenaud, ... Advances in Neural Information Processing Systems 32, 4255--4265, 2019 | 49 | 2019 |
| Understanding short-horizon bias in stochastic meta-optimization Y Wu, M Ren, R Liao, R Grosse International Conference on Learning Representations, 2018 | 48 | 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 | 44 | 2019 |
| 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 | 39 | 2019 |
| Graph partition neural networks for semi-supervised classification R Liao, M Brockschmidt, D Tarlow, AL Gaunt, R Urtasun, R Zemel International Conference on Learning Representations (Workshop Track), 2018 | 37 | 2018 |
Raquel UrtasunProfessor, University of Toronto. Chief Scientist, Uber ATGVerified email at cs.toronto.edu
