| Neural circuit policies enabling auditable autonomy M Lechner, R Hasani, A Amini, TA Henzinger, D Rus, R Grosu Nature Machine Intelligence 2 (10), 642-652, 2020 | 187 | 2020 |
| Liquid time-constant networks R Hasani, M Lechner, A Amini, D Rus, R Grosu AAAI Conference on Artificial Intelligence 35 (9), 7657-7666, 2021 | 157 | 2021 |
| Mixed-Memory RNNs for Learning Long-term Dependencies in Irregularly Sampled Time Series M Lechner, R Hasani NeurIPS 2022 Memory in Artificial and Real Intelligence workshop, 2022 | 110* | 2022 |
| 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 | 58* | 2022 |
| Latent imagination facilitates zero-shot transfer in autonomous racing A Brunnbauer, L Berducci, A Brandstátter, M Lechner, R Hasani, D Rus, ... 2022 international conference on robotics and automation (ICRA), 7513-7520, 2022 | 55* | 2022 |
| Designing worm-inspired neural networks for interpretable robotic control M Lechner, R Hasani, M Zimmer, TA Henzinger, R Grosu International Conference on Robotics and Automation (ICRA), 87-94, 2019 | 44 | 2019 |
| Liquid structural state-space models R Hasani, M Lechner, TH Wang, M Chahine, A Amini, D Rus arXiv preprint arXiv:2209.12951, 2022 | 38 | 2022 |
| Causal navigation by continuous-time neural networks C Vorbach, R Hasani, A Amini, M Lechner, D Rus Advances in Neural Information Processing Systems 34, 12425-12440, 2021 | 38 | 2021 |
| Scalable verification of quantized neural networks TA Henzinger, M Lechner, Đ Žikelić AAAI Conference on Artificial Intelligence 35 (5), 3787-3795, 2021 | 35* | 2021 |
| Adversarial Training is Not Ready for Robot Learning M Lechner, R Hasani, R Grosu, D Rus, TA Henzinger International Conference on Robotics and Automation (ICRA), 4140-4147, 2021 | 34 | 2021 |
| The natural lottery ticket winner: Reinforcement learning with ordinary neural circuits R Hasani, M Lechner, A Amini, D Rus, R Grosu International Conference on Machine Learning (ICML), 2020 | 32* | 2020 |
| On the verification of neural odes with stochastic guarantees S Gruenbacher, R Hasani, M Lechner, J Cyranka, SA Smolka, R Grosu AAAI Conference on Artificial Intelligence 35, 2021 | 30 | 2021 |
| Gershgorin loss stabilizes the recurrent neural network compartment of an end-to-end robot learning scheme M Lechner, R Hasani, D Rus, R Grosu IEEE International Conference on Robotics and Automation (ICRA), 5446-5452, 2020 | 28 | 2020 |
| How Many Bits Does it Take to Quantize Your Neural Network? M Giacobbe, TA Henzinger, M Lechner International Conference on Tools and Algorithms for the Construction and …, 2020 | 27 | 2020 |
| Gotube: Scalable statistical verification of continuous-depth models SA Gruenbacher, M Lechner, R Hasani, D Rus, TA Henzinger, SA Smolka, ... Proceedings of the AAAI Conference on Artificial Intelligence 36 (6), 6755-6764, 2022 | 26 | 2022 |
| Dataset distillation with convexified implicit gradients N Loo, R Hasani, M Lechner, D Rus International Conference on Machine Learning, 22649-22674, 2023 | 24 | 2023 |
| Stability verification in stochastic control systems via neural network supermartingales M Lechner, Đ Žikelić, K Chatterjee, TA Henzinger Proceedings of the AAAI Conference on Artificial Intelligence 36 (7), 7326-7336, 2022 | 19 | 2022 |
| Infinite time horizon safety of bayesian neural networks M Lechner, Đ Žikelić, K Chatterjee, T Henzinger Advances in Neural Information Processing Systems 34, 10171-10185, 2021 | 19 | 2021 |
| Response characterization for auditing cell dynamics in long short-term memory networks R Hasani, A Amini, M Lechner, F Naser, R Grosu, D Rus International Joint Conference on Neural Networks (IJCNN), 1-8, 2019 | 17 | 2019 |
| On-Off Center-Surround Receptive Fields for Accurate and Robust Image Classification Z Babaiee, R Hasani, M Lechner, D Rus, R Grosu International Conference on Machine Learning (ICML), 478-489, 2021 | 15 | 2021 |
Ramin HasaniMassachusetts Institute of TechnologyVerified email at mit.edu
