Peter Ondrúška
Peter Ondrúška
Head of Research, Toyota Woven Planet
在 ondruska.com 的电子邮件经过验证 - 首页
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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
11692016
Maximum entropy deep inverse reinforcement learning
M Wulfmeier, P Ondruska, I Posner
arXiv preprint arXiv:1507.04888, 2015
2452015
Deep tracking: Seeing beyond seeing using recurrent neural networks
P Ondruska, I Posner
Thirtieth AAAI conference on artificial intelligence, 2016
1542016
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
1182015
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
912017
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
792018
Deep tracking: Seeing beyond seeing using recurrent neural networks
P Ondrúška, I Posner
Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence …, 2016
762016
Lyft level 5 av dataset 2019
R Kesten, M Usman, J Houston, T Pandya, K Nadhamuni, A Ferreira, ...
urlhttps://level5. lyft. com/dataset, 2019
722019
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
602016
Deep inverse reinforcement learning
M Wulfmeier, P Ondruska, I Posner
CoRR, abs/1507.04888, 2015
582015
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
532020
Probabilistic attainability maps: Efficiently predicting driver-specific electric vehicle range
P Ondruska, I Posner
2014 IEEE Intelligent Vehicles Symposium Proceedings, 1169-1174, 2014
342014
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
312015
Lyft level 5 perception dataset 2020
R Kesten, M Usman, J Houston, T Pandya, K Nadhamuni, A Ferreira, ...
302019
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
282019
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
262014
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
212016
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
62020
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
52021
Method and system for creating a virtual 3D model
P Ondruska, L Platinsky
US Patent 10,460,511, 2019
22019
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