| Provable Filter Pruning for Efficient Neural Networks L Liebenwein*, C Baykal*, H Lang, D Feldman, D Rus International Conference on Learning Representations, 2020 | 177 | 2020 |
| Data-Dependent Coresets for Compressing Neural Networks with Applications to Generalization Bounds C Baykal*, L Liebenwein*, I Gilitschenski, D Feldman, D Rus International Conference on Learning Representations, 2019 | 91 | 2019 |
| 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 (MLSys 2021), 2021 | 73 | 2021 |
| Closed-form continuous-time neural networks R Hasani, M Lechner, A Amini, L Liebenwein, A Ray, M Tschaikowski, ... Nature Machine Intelligence 4 (11), 992-1003, 2022 | 71* | 2022 |
| Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decomposition L Liebenwein, A Maalouf, D Feldman, D Rus Advances in Neural Information Processing Systems 34, 2021 | 37 | 2021 |
| Deep Latent Competition: Learning to Race Using Visual Control Policies in Latent Space W Schwarting*, T Seyde*, I Gilitschenski*, L Liebenwein, R Sander, ... Conference on Robot Learning (CoRL), 2020 | 34 | 2020 |
| Sensitivity-Informed Provable Pruning of Neural Networks C Baykal*, L Liebenwein*, I Gilitschenski, D Feldman, D Rus SIAM Journal on Mathematics of Data Science 4 (1), 26-45, 2022 | 31* | 2022 |
| Sampling-Based Approximation Algorithms for Reachability Analysis with Provable Guarantees L Liebenwein*, C Baykal*, I Gilitschenski, S Karaman, D Rus Robotics: Science and Systems XIV (RSS), 2018 | 25 | 2018 |
| Compositional and Contract-based Verification for Autonomous Driving on Road Networks L Liebenwein, W Schwarting, CI Vasile, J DeCastro, J Alonso-Mora, ... Robotics Research: The 18th International Symposium ISRR, 2018 | 23 | 2018 |
| Machine Learning-based Estimation of Forest Carbon Stocks to increase Transparency of Forest Preservation Efforts B Lütjens, L Liebenwein, K Kramer arXiv preprint arXiv:1912.07850, 2019 | 17 | 2019 |
| Sparse flows: Pruning continuous-depth models L Liebenwein, R Hasani, A Amini, D Rus Advances in Neural Information Processing Systems 34, 22628-22642, 2021 | 15 | 2021 |
| Counterexample-guided safety contracts for autonomous driving J DeCastro*, L Liebenwein*, CI Vasile, R Tedrake, S Karaman, D Rus International Workshop on the Algorithmic Foundations of Robotics, 2018 | 15 | 2018 |
| Training Support Vector Machines using Coresets C Baykal, L Liebenwein, W Schwarting arXiv preprint arXiv:1708.03835, 2017 | 7 | 2017 |
| Low-Regret Active learning C Baykal, L Liebenwein, D Feldman, D Rus arXiv preprint arXiv:2104.02822, 2021 | 3 | 2021 |
| Publisher Correction: Closed-form continuous-time neural networks R Hasani, M Lechner, A Amini, L Liebenwein, A Ray, M Tschaikowski, ... Nature Machine Intelligence 4 (12), 1267-1267, 2022 | 1 | 2022 |
| Pruning by Active Attention Manipulation Z Babaiee, L Liebenwein, R Hasani, D Rus, R Grosu arXiv preprint arXiv:2210.11114, 2022 | 1* | 2022 |
| Contract-based safety verification for autonomous driving L Liebenwein Massachusetts Institute of Technology, 2018 | 1 | 2018 |
| Closed-form continuous-time neural networks (vol 4, pg 994, 2022) R Hasani, M Lechner, A Amini, L Liebenwein, A Ray, M Tschaikowski, ... NATURE MACHINE INTELLIGENCE 4 (12), 1267-1267, 2022 | | 2022 |
| Efficient Deep Learning: From Theory to Practice L Liebenwein Massachusetts Institute of Technology, 2021 | | 2021 |
| SYSTEM AND METHOD OF VALIDATION OF OPERATIONAL REGULATIONS TO AUTONOMOUSLY OPERATE A VEHICLE DURING TRAVEL J Decastro, L Liebenwein, C Vasile, RL Tedrake, S Karaman, D Rus US Patent App. 16/539,772, 2020 | | 2020 |
Daniela RusAndrew (1956) and Erna Viterbi Professor of Computer Science, MIT在 csail.mit.edu 的电子邮件经过验证
