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Found 7 papers, 1 papers with code
Differentiable Point Scattering Models for Efficient Radar Target Characterization
Target characterization is an important step in many defense missions, often relying on fitting a known target model to observed data.
Tools and Practices for Responsible AI Engineering
Responsible Artificial Intelligence (AI) - the practice of developing, evaluating, and maintaining accurate AI systems that also exhibit essential properties such as robustness and explainability - represents a multifaceted challenge that often stretches standard machine learning tooling, frameworks, and testing methods beyond their limits.
Principles for Evaluation of AI/ML Model Performance and Robustness
The Department of Defense (DoD) has significantly increased its investment in the design, evaluation, and deployment of Artificial Intelligence and Machine Learning (AI/ML) capabilities to address national security needs.
Fast Training of Deep Neural Networks Robust to Adversarial Perturbations
Recent work in adversarial training, a form of robust optimization in which the model is optimized against adversarial examples, demonstrates the ability to improve performance sensitivities to perturbations and yield feature representations that are more interpretable.
Safe Predictors for Enforcing Input-Output Specifications
We present an approach for designing correct-by-construction neural networks (and other machine learning models) that are guaranteed to be consistent with a collection of input-output specifications before, during, and after algorithm training.
Kernelized Capsule Networks
Capsule Networks attempt to represent patterns in images in a way that preserves hierarchical spatial relationships.
AI Enabling Technologies: A Survey
Artificial Intelligence (AI) has the opportunity to revolutionize the way the United States Department of Defense (DoD) and Intelligence Community (IC) address the challenges of evolving threats, data deluge, and rapid courses of action.
