| Characterizing adversarial examples based on spatial consistency information for semantic segmentation C Xiao, R Deng, B Li, F Yu, M Liu, D Song Proceedings of the European Conference on Computer Vision (ECCV), 217-234, 2018 | 103 | 2018 |
| Sparsely aggregated convolutional networks L Zhu, R Deng, M Maire, Z Deng, G Mori, P Tan Proceedings of the European Conference on Computer Vision (ECCV), 186-201, 2018 | 67 | 2018 |
| Advit: Adversarial frames identifier based on temporal consistency in videos C Xiao, R Deng, B Li, T Lee, B Edwards, J Yi, D Song, M Liu, I Molloy Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 50 | 2019 |
| Modeling continuous stochastic processes with dynamic normalizing flows R Deng, B Chang, MA Brubaker, G Mori, A Lehrmann Advances in Neural Information Processing Systems 33, 7805-7815, 2020 | 44 | 2020 |
| Continuous latent process flows R Deng, MA Brubaker, G Mori, A Lehrmann Advances in Neural Information Processing Systems 34, 5162-5173, 2021 | 14 | 2021 |
| Point process flows N Mehrasa, R Deng, MO Ahmed, B Chang, J He, T Durand, M Brubaker, ... arXiv preprint arXiv:1910.08281, 2019 | 14 | 2019 |
| Sparsely connected convolutional networks L Zhu, R Deng, Z Deng, G Mori, P Tan arXiv preprint arXiv:1801.05895, 2018 | 4 | 2018 |
| Learning to forecast videos of human activity with multi-granularity models and adaptive rendering M Zhai, J Chen, R Deng, L Chen, L Zhu, G Mori arXiv preprint arXiv:1712.01955, 2017 | 3 | 2017 |
| Systems and methods for modeling continuous stochastic processes with dynamic normalizing flows D Ruizhi, B Chang, MA Brubaker, GP Mori, ASM Lehrmann US Patent App. 17/170,416, 2021 | 2 | 2021 |
| Adaptive appearance rendering M Zhai, R Deng, J Chen, L Chen, Z Deng, G Mori arXiv preprint arXiv:2104.11931, 2021 | 2 | 2021 |
| Variational hyper rnn for sequence modeling R Deng, Y Cao, B Chang, L Sigal, G Mori, MA Brubaker arXiv preprint arXiv:2002.10501, 2020 | 2 | 2020 |
| System and method for machine learning architecture with variational hyper-RNN D Ruizhi, CAO Yanshuai, B Chang, M Brubaker US Patent 11,615,305, 2023 | 1 | 2023 |
| System and method for continuous dynamics model from irregular time-series data D Ruizhi, MA Brubaker, GP Mori, ASM Lehrmann US Patent App. 17/749,678, 2022 | | 2022 |
| Continuous-time Particle Filtering for Latent Stochastic Differential Equations R Deng, G Mori, AM Lehrmann arXiv preprint arXiv:2209.00173, 2022 | | 2022 |
| Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows Supplementary Materials R Deng, B Chang, MA Brubaker, G Mori, A Lehrmann | | |
Greg MoriResearch Director, Borealis AI; Professor, School of Computing Science, Simon Fraser UniversityVerified email at cs.sfu.ca
