Search Results for author:
Found 4 papers, 0 papers with code
Test & Evaluation Best Practices for Machine Learning-Enabled Systems
This report presents best practices for the Test and Evaluation (T&E) of ML-enabled software systems across its lifecycle.
Evaluating Automated Driving Planner Robustness against Adversarial Influence
In contrast with established practices that evaluate safety using the same evaluation dataset for all vehicles, we argue that adversarial evaluation fundamentally requires a process that seeks to defeat a specific protection.
Systematic Training and Testing for Machine Learning Using Combinatorial Interaction Testing
In contrast to prior work which has focused on the use of coverage in regard to the internal of neural networks, this paper considers coverage over simple features derived from inputs and outputs.
Test and Evaluation Framework for Multi-Agent Systems of Autonomous Intelligent Agents
Test and evaluation is a necessary process for ensuring that engineered systems perform as intended under a variety of conditions, both expected and unexpected.
