119 citations · 126 across the 3 of their papers we have counts for
4 papers · 1 filter
Global Optimization of Objective Functions Represented by ReLU Networks
Christopher A. Strong, Haoze Wu, Aleksandar Zeljić +4
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 failur…
Deep Neural Network Compression for Aircraft Collision Avoidance Systems
Kyle D. Julian, Mykel J. Kochenderfer, Michael P. Owen
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 pr…
Decomposition Methods with Deep Corrections for Reinforcement Learning
Maxime Bouton, Kyle Julian, Alireza Nakhaei +2
Decomposition methods have been proposed to approximate solutions to large sequential decision making problems. In contexts where an agent interacts with multiple entities, utility…
Towards Proving the Adversarial Robustness of Deep Neural Networks
Guy Katz, Clark Barrett, David L. Dill +2
Autonomous vehicles are highly complex systems, required to function reliably in a wide variety of situations. Manually crafting software controllers for these vehicles is difficul…