Search Results for author:
Found 5 papers, 2 papers with code
Verification for Object Detection -- IBP IoU
We introduce a novel Interval Bound Propagation (IBP) approach for the formal verification of object detection models, specifically targeting the Intersection over Union (IoU) metric.
Formal description of ML models for unambiguous implementation
Implementing deep neural networks in safety critical systems, in particular in the aeronautical domain, will require to offer adequate specification paradigms to preserve the semantics of the trained model on the final hardware platform.
LARD - Landing Approach Runway Detection -- Dataset for Vision Based Landing
As the interest in autonomous systems continues to grow, one of the major challenges is collecting sufficient and representative real-world data.
White Paper Machine Learning in Certified Systems
Machine Learning (ML) seems to be one of the most promising solution to automate partially or completely some of the complex tasks currently realized by humans, such as driving vehicles, recognizing voice, etc.
Safety Verification of Neural Network Controlled Systems
In this paper, we propose a system-level approach for verifying the safety of neural network controlled systems, combining a continuous-time physical system with a discrete-time neural network based controller.
