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Found 6 papers, 3 papers with code
Generating Probabilistic Safety Guarantees for Neural Network Controllers
In this work, we develop a method to use the results from neural network verification tools to provide probabilistic safety guarantees on a neural network controller.
Global Optimization of Objective Functions Represented by ReLU Networks
However, individual "yes or no" questions cannot answer qualitative questions such as "what is the largest error within these bounds"; the answers to these lie in the domain of optimization.
Guaranteeing Safety for Neural Network-Based Aircraft Collision Avoidance Systems
The neural network outputs are bounded using neural network verification tools like Reluplex and Reluval, and a reachability method determines all possible ways aircraft encounters will resolve using neural network advisories and assuming bounded aircraft dynamics.
Deep Neural Network Compression for Aircraft Collision Avoidance Systems
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.
Distributed Wildfire Surveillance with Autonomous Aircraft using Deep Reinforcement Learning
Teams of autonomous unmanned aircraft can be used to monitor wildfires, enabling firefighters to make informed decisions.
Image-based Guidance of Autonomous Aircraft for Wildfire Surveillance and Prediction
The second approach uses a particle filter to predict wildfire growth and uses observations to estimate uncertainties relating to wildfire expansion.
