| Ask me anything: Dynamic memory networks for natural language processing A Kumar, O Irsoy, P Ondruska, M Iyyer, J Bradbury, I Gulrajani, V Zhong, ... International conference on machine learning, 1378-1387, 2016 | 1169 | 2016 |
| Maximum entropy deep inverse reinforcement learning M Wulfmeier, P Ondruska, I Posner arXiv preprint arXiv:1507.04888, 2015 | 245 | 2015 |
| Deep tracking: Seeing beyond seeing using recurrent neural networks P Ondruska, I Posner Thirtieth AAAI conference on artificial intelligence, 2016 | 154 | 2016 |
| Mobilefusion: Real-time volumetric surface reconstruction and dense tracking on mobile phones P Ondrúška, P Kohli, S Izadi IEEE transactions on visualization and computer graphics 21 (11), 1251-1258, 2015 | 118 | 2015 |
| Large-scale cost function learning for path planning using deep inverse reinforcement learning M Wulfmeier, D Rao, DZ Wang, P Ondruska, I Posner The International Journal of Robotics Research 36 (10), 1073-1087, 2017 | 91 | 2017 |
| Deep tracking in the wild: End-to-end tracking using recurrent neural networks J Dequaire, P Ondrúška, D Rao, D Wang, I Posner The International Journal of Robotics Research 37 (4-5), 492-512, 2018 | 79 | 2018 |
| Deep tracking: Seeing beyond seeing using recurrent neural networks P Ondrúška, I Posner Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence …, 2016 | 76 | 2016 |
| Lyft level 5 av dataset 2019 R Kesten, M Usman, J Houston, T Pandya, K Nadhamuni, A Ferreira, ... urlhttps://level5. lyft. com/dataset, 2019 | 72 | 2019 |
| End-to-end tracking and semantic segmentation using recurrent neural networks P Ondruska, J Dequaire, DZ Wang, I Posner arXiv preprint arXiv:1604.05091, 2016 | 60 | 2016 |
| Deep inverse reinforcement learning M Wulfmeier, P Ondruska, I Posner CoRR, abs/1507.04888, 2015 | 58 | 2015 |
| One thousand and one hours: Self-driving motion prediction dataset J Houston, G Zuidhof, L Bergamini, Y Ye, L Chen, A Jain, S Omari, ... arXiv preprint arXiv:2006.14480, 2020 | 53 | 2020 |
| Probabilistic attainability maps: Efficiently predicting driver-specific electric vehicle range P Ondruska, I Posner 2014 IEEE Intelligent Vehicles Symposium Proceedings, 1169-1174, 2014 | 34 | 2014 |
| Scheduled perception for energy-efficient path following P Ondrúška, C Gurău, L Marchegiani, CH Tong, I Posner 2015 IEEE International Conference on Robotics and Automation (ICRA), 4799-4806, 2015 | 31 | 2015 |
| Lyft level 5 perception dataset 2020 R Kesten, M Usman, J Houston, T Pandya, K Nadhamuni, A Ferreira, ... | 30 | 2019 |
| Lyft level 5 av dataset 2019. urlhttps R Kesten, M Usman, J Houston, T Pandya, K Nadhamuni, A Ferreira, ... level5. lyft. com/dataset 2, 5, 2019 | 28 | 2019 |
| The route not taken: Driver-centric estimation of electric vehicle range P Ondruska, I Posner Twenty-Fourth International Conference on Automated Planning and Scheduling, 2014 | 26 | 2014 |
| Deep tracking on the move: Learning to track the world from a moving vehicle using recurrent neural networks J Dequaire, D Rao, P Ondruska, D Wang, I Posner arXiv preprint arXiv:1609.09365, 2016 | 21 | 2016 |
| Collaborative Augmented Reality on Smartphones via Life-long City-scale Maps L Platinsky, M Szabados, F Hlasek, R Hemsley, L Del Pero, A Pancik, ... 2020 IEEE International Symposium on Mixed and Augmented Reality (ISMAR …, 2020 | 6 | 2020 |
| SimNet: Learning Reactive Self-driving Simulations from Real-world Observations L Bergamini, Y Ye, O Scheel, L Chen, C Hu, L Del Pero, B Osinski, ... arXiv preprint arXiv:2105.12332, 2021 | 5 | 2021 |
| Method and system for creating a virtual 3D model P Ondruska, L Platinsky US Patent 10,460,511, 2019 | 2 | 2019 |
Ingmar PosnerOxford University在 robots.ox.ac.uk 的电子邮件经过验证
