| Reluplex: An efficient SMT solver for verifying deep neural networks G Katz, C Barrett, DL Dill, K Julian, MJ Kochenderfer International conference on computer aided verification, 97-117, 2017 | 1302 | 2017 |
| The marabou framework for verification and analysis of deep neural networks G Katz, DA Huang, D Ibeling, K Julian, C Lazarus, R Lim, P Shah, ... International Conference on Computer Aided Verification, 443-452, 2019 | 246 | 2019 |
| Policy compression for aircraft collision avoidance systems KD Julian, J Lopez, JS Brush, MP Owen, MJ Kochenderfer 2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC), 1-10, 2016 | 188 | 2016 |
| Deep neural network compression for aircraft collision avoidance systems KD Julian, MJ Kochenderfer, MP Owen Journal of Guidance, Control, and Dynamics 42 (3), 598-608, 2019 | 115 | 2019 |
| Towards proving the adversarial robustness of deep neural networks G Katz, C Barrett, DL Dill, K Julian, MJ Kochenderfer arXiv preprint arXiv:1709.02802, 2017 | 109 | 2017 |
| Distributed wildfire surveillance with autonomous aircraft using deep reinforcement learning KD Julian, MJ Kochenderfer Journal of Guidance, Control, and Dynamics 42 (8), 1768-1778, 2019 | 59 | 2019 |
| Neural network guidance for UAVs KD Julian, MJ Kochenderfer AIAA Guidance, Navigation, and Control Conference, 1743, 2017 | 37 | 2017 |
| Toward scalable verification for safety-critical deep networks L Kuper, G Katz, J Gottschlich, K Julian, C Barrett, M Kochenderfer arXiv preprint arXiv:1801.05950, 2018 | 33 | 2018 |
| Guaranteeing safety for neural network-based aircraft collision avoidance systems KD Julian, MJ Kochenderfer 2019 IEEE/AIAA 38th Digital Avionics Systems Conference (DASC), 1-10, 2019 | 32 | 2019 |
| Parallelization techniques for verifying neural networks H Wu, A Ozdemir, A Zeljic, K Julian, A Irfan, D Gopinath, S Fouladi, G Katz, ... # PLACEHOLDER_PARENT_METADATA_VALUE# 1, 128-137, 2020 | 27 | 2020 |
| Validation of image-based neural network controllers through adaptive stress testing KD Julian, R Lee, MJ Kochenderfer 2020 IEEE 23rd international conference on intelligent transportation …, 2020 | 22 | 2020 |
| Verifying aircraft collision avoidance neural networks through linear approximations of safe regions KD Julian, S Sharma, JB Jeannin, MJ Kochenderfer arXiv preprint arXiv:1903.00762, 2019 | 18 | 2019 |
| A reachability method for verifying dynamical systems with deep neural network controllers KD Julian, MJ Kochenderfer arXiv preprint arXiv:1903.00520, 2019 | 14 | 2019 |
| Global optimization of objective functions represented by ReLU networks CA Strong, H Wu, A Zeljiĉ, KD Julian, G Katz, C Barrett, MJ Kochenderfer Machine Learning, 1-28, 2021 | 13 | 2021 |
| Reluplex: a calculus for reasoning about deep neural networks G Katz, C Barrett, DL Dill, K Julian, MJ Kochenderfer Formal Methods in System Design, 1-30, 2021 | 13 | 2021 |
| Autonomous distributed wildfire surveillance using deep reinforcement learning KD Julian, MJ Kochenderfer 2018 AIAA Guidance, Navigation, and Control Conference, 1589, 2018 | 12 | 2018 |
| Reachability analysis for neural network aircraft collision avoidance systems KD Julian, MJ Kochenderfer Journal of Guidance, Control, and Dynamics 44 (6), 1132-1142, 2021 | 11 | 2021 |
| Utility decomposition with deep corrections for scalable planning under uncertainty M Bouton, K Julian, A Nakhaei, K Fujimura, MJ Kochenderfer Proceedings of the 17th International Conference on Autonomous Agents and …, 2018 | 11 | 2018 |
| Application of machine learning to link prediction K Julian, W Lu arXiv, 2016 | 10 | 2016 |
| UAV depth perception from visual images using a deep convolutional neural network K Julian, J Mern, R Tompa Tech. Rep., 2017 | 8 | 2017 |
Mykel J. KochenderferAssociate Professor, Stanford UniversityVerified email at stanford.edu
