Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Julian, Kyle D."'
Publikováno v:
Mach Learn (2021). http://link.springer.com/article/10.1007/s10994-021-06065-9
Neural networks serve as effective controllers in a variety of complex settings due to their ability to represent expressive policies. The complex nature of neural networks, however, makes their output difficult to verify and predict, which limits th
Externí odkaz:
http://arxiv.org/abs/2103.01203
Autor:
Strong, Christopher A., Wu, Haoze, Zeljić, Aleksandar, Julian, Kyle D., Katz, Guy, Barrett, Clark, Kochenderfer, Mykel J.
Neural networks can learn complex, non-convex functions, and it is challenging to guarantee their correct behavior in safety-critical contexts. Many approaches exist to find failures in networks (e.g., adversarial examples), but these cannot guarante
Externí odkaz:
http://arxiv.org/abs/2010.03258
Neural networks have become state-of-the-art for computer vision problems because of their ability to efficiently model complex functions from large amounts of data. While neural networks can be shown to perform well empirically for a variety of task
Externí odkaz:
http://arxiv.org/abs/2003.02381
Publikováno v:
IEEE/AIAA 38th Digital Avionics Systems Conference (DASC). 2019
The decision logic for the ACAS X family of aircraft collision avoidance systems is represented as a large numeric table. Due to storage constraints of certified avionics hardware, neural networks have been suggested as a way to significantly compres
Externí odkaz:
http://arxiv.org/abs/1912.07084
Verifying Aircraft Collision Avoidance Neural Networks Through Linear Approximations of Safe Regions
The next generation of aircraft collision avoidance systems frame the problem as a Markov decision process and use dynamic programming to optimize the alerting logic. The resulting system uses a large lookup table to determine advisories given to pil
Externí odkaz:
http://arxiv.org/abs/1903.00762
Deep neural networks can be trained to be efficient and effective controllers for dynamical systems; however, the mechanics of deep neural networks are complex and difficult to guarantee. This work presents a general approach for providing guarantees
Externí odkaz:
http://arxiv.org/abs/1903.00520
Teams of autonomous unmanned aircraft can be used to monitor wildfires, enabling firefighters to make informed decisions. However, controlling multiple autonomous fixed-wing aircraft to maximize forest fire coverage is a complex problem. The state sp
Externí odkaz:
http://arxiv.org/abs/1810.04244
One approach to designing decision making logic for an aircraft collision avoidance system frames the problem as a Markov decision process and optimizes the system using dynamic programming. The resulting collision avoidance strategy can be represent
Externí odkaz:
http://arxiv.org/abs/1810.04240
Small unmanned aircraft can help firefighters combat wildfires by providing real-time surveillance of the growing fires. However, guiding the aircraft autonomously given only wildfire images is a challenging problem. This work models noisy images obt
Externí odkaz:
http://arxiv.org/abs/1810.02455
Autor:
Strong, Christopher A., Wu, Haoze, Zeljić, Aleksandar, Julian, Kyle D., Katz, Guy, Barrett, Clark, Kochenderfer, Mykel J.
Publikováno v:
Machine Learning; Oct2023, Vol. 112 Issue 10, p3685-3712, 28p
