no code implementations • 17 Apr 2020 • Haoze Wu, Alex Ozdemir, Aleksandar Zeljić, Ahmed Irfan, Kyle Julian, Divya Gopinath, Sadjad Fouladi, Guy Katz, Corina Pasareanu, Clark Barrett
Inspired by recent successes with parallel optimization techniques for solving Boolean satisfiability, we investigate a set of strategies and heuristics that aim to leverage parallel computing to improve the scalability of neural network verification.
no code implementations • 10 Dec 2018 • John Mern, Kyle Julian, Rachael E. Tompa, Mykel J. Kochenderfer
A reliable sense-and-avoid system is critical to enabling safe autonomous operation of unmanned aircraft.
1 code implementation • 6 Feb 2018 • Maxime Bouton, Kyle Julian, Alireza Nakhaei, Kikuo Fujimura, Mykel J. Kochenderfer
In contexts where an agent interacts with multiple entities, utility decomposition can be used to separate the global objective into local tasks considering each individual entity independently.
no code implementations • 18 Jan 2018 • Lindsey Kuper, Guy Katz, Justin Gottschlich, Kyle Julian, Clark Barrett, Mykel Kochenderfer
The increasing use of deep neural networks for safety-critical applications, such as autonomous driving and flight control, raises concerns about their safety and reliability.
no code implementations • 8 Sep 2017 • Guy Katz, Clark Barrett, David L. Dill, Kyle Julian, Mykel J. Kochenderfer
Autonomous vehicles are highly complex systems, required to function reliably in a wide variety of situations.
7 code implementations • 3 Feb 2017 • Guy Katz, Clark Barrett, David Dill, Kyle Julian, Mykel Kochenderfer
Deep neural networks have emerged as a widely used and effective means for tackling complex, real-world problems.