Existing frameworks for transfer learning are incomplete from a systems theoretic
perspective. They place emphasis on notions of domain and task, and neglect notions of …

Classical machine learning approachesare sensitive to nonstationarity. Transfer learning
can address nonstationarity by sharing knowledge from one system to another, however, in …

Intelligent systems ought to be distinguished as a special type of system. While some adopt
this view informally, in practice, systems engineering methods for intelligent systems are still …

The machine learning formulation of online learning is incomplete from a systems theoretic
perspective. Typically, machine learning research emphasizes domains and tasks, and a …

This paper addresses the urgent need for messaging standards in the operational test and
evaluation (T&E) of machine learning (ML) applications, particularly in edge ML applications …

Transfer learning, multi-task learning, and meta-learning are well-studied topics concerned
with the generalization of knowledge across learning tasks and are closely related to …

In recent years, engineering intelligent systems has become closely related to integrating
artificial intelligence (AI) or machine learning (ML) components into systems. There is a …

There is a growing orientation of cyber systems, technologies, and processes away from
notions of cybersecurity and towards notions of cyber resilience. In multi-domain operation …

With the increasing use of machine learning in autonomous systems, there is both an
increasing need and interest in models of machine learning that are compatible with models …

There is an increasing demand for operational uses of machine learning (ML), however, a
lack of best practices for test and evaluation (T &E) of learning systems is a hindrance to …