Kyle Julian

Orcid: 0000-0002-6247-1874

According to our database1, Kyle Julian authored at least 19 papers between 2017 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
Marabou 2.0: A Versatile Formal Analyzer of Neural Networks.
Proceedings of the Computer Aided Verification - 36th International Conference, 2024

2023
Global optimization of objective functions represented by ReLU networks.
Mach. Learn., October, 2023

Generating probabilistic safety guarantees for neural network controllers.
Mach. Learn., 2023

2022
Reluplex: a calculus for reasoning about deep neural networks.
Formal Methods Syst. Des., February, 2022

2020
Validation of Image-Based Neural Network Controllers through Adaptive Stress Testing.
Proceedings of the 23rd IEEE International Conference on Intelligent Transportation Systems, 2020

Parallelization Techniques for Verifying Neural Networks.
Proceedings of the 2020 Formal Methods in Computer Aided Design, 2020

2019
Guaranteeing Safety for Neural Network-Based Aircraft Collision Avoidance Systems.
CoRR, 2019

Verifying Aircraft Collision Avoidance Neural Networks Through Linear Approximations of Safe Regions.
CoRR, 2019

A Reachability Method for Verifying Dynamical Systems with Deep Neural Network Controllers.
CoRR, 2019

Decomposition methods with deep corrections for reinforcement learning.
Auton. Agents Multi Agent Syst., 2019

The Marabou Framework for Verification and Analysis of Deep Neural Networks.
Proceedings of the Computer Aided Verification - 31st International Conference, 2019

2018
Visual Depth Mapping from Monocular Images using Recurrent Convolutional Neural Networks.
CoRR, 2018

Distributed Wildfire Surveillance with Autonomous Aircraft using Deep Reinforcement Learning.
CoRR, 2018

Deep Neural Network Compression for Aircraft Collision Avoidance Systems.
CoRR, 2018

Image-based Guidance of Autonomous Aircraft for Wildfire Surveillance and Prediction.
CoRR, 2018

Toward Scalable Verification for Safety-Critical Deep Networks.
CoRR, 2018

Utility Decomposition with Deep Corrections for Scalable Planning under Uncertainty.
Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, 2018

2017
Towards Proving the Adversarial Robustness of Deep Neural Networks.
Proceedings of the Proceedings First Workshop on Formal Verification of Autonomous Vehicles, 2017

Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks.
Proceedings of the Computer Aided Verification - 29th International Conference, 2017


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