フォロー
Kyle David Julian
Kyle David Julian
Received Ph.D. at Stanford University
確認したメール アドレス: stanford.edu
タイトル
引用先
引用先
Reluplex: An efficient SMT solver for verifying deep neural networks
G Katz, C Barrett, DL Dill, K Julian, MJ Kochenderfer
Computer Aided Verification: 29th International Conference, CAV 2017 …, 2017
24032017
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, ...
Computer Aided Verification: 31st International Conference, CAV 2019, New …, 2019
6852019
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
3222016
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
2282019
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
1532017
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
1412019
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
742019
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
662020
Reluplex: a calculus for reasoning about deep neural networks
G Katz, C Barrett, DL Dill, K Julian, MJ Kochenderfer
Formal Methods in System Design 60 (1), 87-116, 2022
582022
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
482018
Neural network guidance for UAVs
KD Julian, MJ Kochenderfer
AIAA Guidance, Navigation, and Control Conference, 1743, 2017
482017
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
462020
Global optimization of objective functions represented by ReLU networks
CA Strong, H Wu, A Zeljić, KD Julian, G Katz, C Barrett, MJ Kochenderfer
Machine Learning 112 (10), 3685-3712, 2023
382023
A reachability method for verifying dynamical systems with deep neural network controllers
KD Julian, MJ Kochenderfer
arXiv preprint arXiv:1903.00520, 2019
372019
Reachability analysis for neural network aircraft collision avoidance systems
KD Julian, MJ Kochenderfer
Journal of Guidance, Control, and Dynamics 44 (6), 1132-1142, 2021
322021
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
312019
Marabou 2.0: a versatile formal analyzer of neural networks
H Wu, O Isac, A Zeljić, T Tagomori, M Daggitt, W Kokke, I Refaeli, G Amir, ...
International Conference on Computer Aided Verification, 249-264, 2024
222024
Towards verification of neural networks for small unmanned aircraft collision avoidance
A Irfan, KD Julian, H Wu, C Barrett, MJ Kochenderfer, B Meng, J Lopez
2020 AIAA/IEEE 39th Digital Avionics Systems Conference (DASC), 1-10, 2020
212020
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
202018
Autonomous distributed wildfire surveillance using deep reinforcement learning
KD Julian, MJ Kochenderfer
2018 AIAA guidance, navigation, and control conference, 1589, 2018
172018
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