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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
21982017
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
6002019
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
2962016
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
2062019
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
1502017
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
1302019
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
642019
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
592020
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
512022
Neural network guidance for UAVs
KD Julian, MJ Kochenderfer
AIAA Guidance, Navigation, and Control Conference, 1743, 2017
462017
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
452018
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
412020
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
352023
Reachability analysis for neural network aircraft collision avoidance systems
KD Julian, MJ Kochenderfer
Journal of Guidance, Control, and Dynamics 44 (6), 1132-1142, 2021
312021
A reachability method for verifying dynamical systems with deep neural network controllers
KD Julian, MJ Kochenderfer
arXiv preprint arXiv:1903.00520, 2019
312019
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
302019
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
182018
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
172020
Autonomous distributed wildfire surveillance using deep reinforcement learning
KD Julian, MJ Kochenderfer
2018 AIAA guidance, navigation, and control conference, 1589, 2018
152018
Decomposition methods with deep corrections for reinforcement learning
M Bouton, KD Julian, A Nakhaei, K Fujimura, MJ Kochenderfer
Autonomous Agents and Multi-Agent Systems 33, 330-352, 2019
112019
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