| Provable filter pruning for efficient neural networks L Liebenwein*, C Baykal*, H Lang, D Feldman, D Rus arXiv preprint arXiv:1911.07412, 2019 | 184 | 2019 |
| Data-dependent coresets for compressing neural networks with applications to generalization bounds C Baykal*, L Liebenwein*, I Gilitschenski, D Feldman, D Rus arXiv preprint arXiv:1804.05345, 2018 | 92 | 2018 |
| Lost in pruning: The effects of pruning neural networks beyond test accuracy L Liebenwein, C Baykal, B Carter, D Gifford, D Rus Proceedings of Machine Learning and Systems 3, 93-138, 2021 | 77 | 2021 |
| Interactive-rate motion planning for concentric tube robots LG Torres, C Baykal, R Alterovitz 2014 IEEE international conference on robotics and automation (ICRA), 1915-1921, 2014 | 59 | 2014 |
| On coresets for support vector machines M Tukan*, C Baykal*, D Feldman, D Rus International Conference on Theory and Applications of Models of Computation …, 2020 | 42 | 2020 |
| Optimizing design parameters for sets of concentric tube robots using sampling-based motion planning C Baykal, LG Torres, R Alterovitz 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2015 | 40 | 2015 |
| Asymptotically Optimal Design of Piecewise Cylindrical Robots using Motion Planning. C Baykal, R Alterovitz Robotics: science and systems 2017, 2017 | 33 | 2017 |
| SiPPing Neural Networks: Sensitivity-informed Provable Pruning of Neural Networks C Baykal*, L Liebenwein*, I Gilitschenski, D Feldman, D Rus arXiv preprint arXiv:1910.05422, 2019 | 32* | 2019 |
| Asymptotically optimal kinematic design of robots using motion planning C Baykal, C Bowen, R Alterovitz Autonomous robots 43 (2), 345-357, 2019 | 31 | 2019 |
| Sampling-based approximation algorithms for reachability analysis with provable guarantees L Liebenwein*, C Baykal*, I Gilitschenski, S Karaman, D Rus | 26 | 2018 |
| Resilient multi-agent consensus using wi-fi signals S Gil, C Baykal, D Rus IEEE control systems letters 3 (1), 126-131, 2018 | 22 | 2018 |
| Kinematic design optimization of a parallel surgical robot to maximize anatomical visibility via motion planning A Kuntz, C Bowen, C Baykal, AW Mahoney, PL Anderson, F Maldonado, ... 2018 IEEE International Conference on Robotics and Automation (ICRA), 926-933, 2018 | 17 | 2018 |
| Participatory route planning D Wilkie, C Baykal, MC Lin Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances …, 2014 | 14 | 2014 |
| Persistent surveillance of events with unknown, time-varying statistics C Baykal, G Rosman, S Claici, D Rus 2017 IEEE International Conference on Robotics and Automation (ICRA), 2682-2689, 2017 | 10 | 2017 |
| A theoretical view on sparsely activated networks C Baykal, N Dikkala, R Panigrahy, C Rashtchian, X Wang Advances in Neural Information Processing Systems 35, 30071-30084, 2022 | 9 | 2022 |
| Robust active distillation C Baykal, K Trinh, F Iliopoulos, G Menghani, E Vee arXiv preprint arXiv:2210.01213, 2022 | 9 | 2022 |
| Weighted distillation with unlabeled examples F Iliopoulos, V Kontonis, C Baykal, G Menghani, K Trinh, E Vee Advances in Neural Information Processing Systems 35, 7024-7037, 2022 | 8 | 2022 |
| Graph belief propagation networks J Jia, C Baykal, VK Potluru, AR Benson arXiv preprint arXiv:2106.03033, 2021 | 8 | 2021 |
| Training support vector machines using coresets C Baykal, L Liebenwein, W Schwarting arXiv preprint arXiv:1708.03835, 2017 | 7 | 2017 |
| Persistent Surveillance of Events with Unknown Rate Statistics C Baykal, G Rosman, K Kotowick, M Donahue, D Rus | 5* | 2016 |
Daniela RusAndrew (1956) and Erna Viterbi Professor of Computer Science, MIT在 csail.mit.edu 的电子邮件经过验证
