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Kyle David Julian
Kyle David Julian
Received Ph.D. at Stanford University
Bestätigte E-Mail-Adresse bei stanford.edu
Titel
Zitiert von
Zitiert von
Jahr
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
23052017
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
6422019
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
3082016
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
2172019
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
1372019
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
692019
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
642020
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
542022
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
472018
Neural network guidance for UAVs
KD Julian, MJ Kochenderfer
AIAA Guidance, Navigation, and Control Conference, 1743, 2017
462017
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
432020
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
372023
A reachability method for verifying dynamical systems with deep neural network controllers
KD Julian, MJ Kochenderfer
arXiv preprint arXiv:1903.00520, 2019
332019
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
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
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
182020
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
152024
Autonomous distributed wildfire surveillance using deep reinforcement learning
KD Julian, MJ Kochenderfer
2018 AIAA guidance, navigation, and control conference, 1589, 2018
152018
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