| Active Learning for Convolutional Neural Networks: A Core-Set Approach O Sener, S Savarese International Conference on Learning Representations (ICLR), 2018 | 1884 | 2018 |
| 3d semantic parsing of large-scale indoor spaces I Armeni, O Sener, AR Zamir, H Jiang, I Brilakis, M Fischer, S Savarese Proceedings of the IEEE conference on computer vision and pattern …, 2016 | 1836 | 2016 |
| Multi-task learning as multi-objective optimization O Sener, V Koltun Advances in neural information processing systems 31, 2018 | 1174 | 2018 |
| Generalizing to unseen domains via adversarial data augmentation R Volpi, H Namkoong, O Sener, JC Duchi, V Murino, S Savarese Advances in neural information processing systems 31, 2018 | 807 | 2018 |
| Learning transferrable representations for unsupervised domain adaptation O Sener, HO Song, A Saxena, S Savarese Advances in neural information processing systems 29, 2016 | 337 | 2016 |
| Robobrain: Large-scale knowledge engine for robots A Saxena, A Jain, O Sener, A Jami, DK Misra, HS Koppula arXiv preprint arXiv:1412.0691, 2014 | 218 | 2014 |
| Watch-n-Patch: Unsupervised Learning of Actions and Relations C Wu, J Zhang, O Sener, B Selman, S Savarese, A Saxena IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017 | 196* | 2017 |
| MSeg: A composite dataset for multi-domain semantic segmentation J Lambert, Z Liu, O Sener, J Hays, V Koltun Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 190 | 2020 |
| Unsupervised semantic parsing of video collections O Sener, AR Zamir, S Savarese, A Saxena Proceedings of the IEEE International conference on Computer Vision, 4480-4488, 2015 | 124 | 2015 |
| Deep learning under privileged information using heteroscedastic dropout J Lambert, O Sener, S Savarese Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 108 | 2018 |
| Online continual learning with natural distribution shifts: An empirical study with visual data Z Cai, O Sener, V Koltun Proceedings of the IEEE/CVF international conference on computer vision …, 2021 | 78 | 2021 |
| Automatically learning and controlling connected devices A Saxena, HS Koppula, C Wu, O Sener US Patent 10,270,609, 2019 | 64 | 2019 |
| Hausdorff dimension, heavy tails, and generalization in neural networks U Simsekli, O Sener, G Deligiannidis, MA Erdogdu Advances in Neural Information Processing Systems 33, 5138-5151, 2020 | 62* | 2020 |
| image2mass: Estimating the mass of an object from its image T Standley, O Sener, D Chen, S Savarese Conference on Robot Learning, 324-333, 2017 | 56 | 2017 |
| Error-tolerant interactive image segmentation by using dynamic and iterated graph-cuts O Sener, K Ugur, A Alatan ACM Multimedia 2012 Workshop IMMPD, 2012 | 32 | 2012 |
| Learning a Generative Model for Multi‐Step Human‐Object Interactions from Videos H Wang, S Pirk, E Yumer, VG Kim, O Sener, S Sridhar, LJ Guibas Computer Graphics Forum 38 (2), 367-378, 2019 | 28 | 2019 |
| Modeling and optimization trade-off in meta-learning K Gao, O Sener Advances in Neural Information Processing Systems 33, 11154-11165, 2020 | 27 | 2020 |
| Drinking from a firehose: Continual learning with web-scale natural language H Hu, O Sener, F Sha, V Koltun IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (5), 5684-5696, 2022 | 26 | 2022 |
| rCRF: Recursive Belief Estimation over CRFs in RGB-D Activity Videos. O Sener, A Saxena Robotics: Science and systems, 2015 | 25 | 2015 |
| A stochastic derivative free optimization method with momentum E Gorbunov, A Bibi, O Sener, EH Bergou, P Richtárik arXiv preprint arXiv:1905.13278, 2019 | 24 | 2019 |
Silvio SavareseAssociate Professor of Computer Science at Stanford University在 stanford.edu 的电子邮件经过验证
