2 citations · 4 across the 7 of their papers we have counts for
8 papers
GENUINE: Graph Enhanced Multi-level Uncertainty Estimation for Large Language Models
Tuo Wang, Adithya Kulkarni, Tyler Cody +3
Uncertainty estimation is essential for enhancing the reliability of Large Language Models (LLMs), particularly in high-stakes applications. Existing methods often overlook semanti…
From Capabilities to Performance: Evaluating Key Functional Properties of LLM Architectures in Penetration Testing
Lanxiao Huang, Daksh Dave, Tyler Cody +2
Large language models (LLMs) are increasingly used to automate or augment penetration testing, but their effectiveness and reliability across attack phases remain unclear. We prese…
Exploring Core and Periphery Precepts in Biological and Artificial Intelligence: An Outcome-Based Perspective
Niloofar Shadab, Tyler Cody, Alejandro Salado +3
Engineering methodologies predominantly revolve around established principles of decomposition and recomposition. These principles involve partitioning inputs and outputs at the co…
Defense against Joint Poison and Evasion Attacks: A Case Study of DERMS
Zain ul Abdeen, Padmaksha Roy, Ahmad Al-Tawaha +6
There is an upward trend of deploying distributed energy resource management systems (DERMS) to control modern power grids. However, DERMS controller communication lines are vulner…
A Systems Theoretic Approach to Online Machine Learning
Anli du Preez, Peter A. Beling, Tyler Cody
The machine learning formulation of online learning is incomplete from a systems theoretic perspective. Typically, machine learning research emphasizes domains and tasks, and a pro…
On Extending the Automatic Test Markup Language (ATML) for Machine Learning
Tyler Cody, Bingtong Li, Peter A. Beling
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 application…
A Systems-Theoretical Formalization of Closed Systems
Niloofar Shadab, Tyler Cody, Alejandro Salado +1
There is a lack of formalism for some key foundational concepts in systems engineering. One of the most recently acknowledged deficits is the inadequacy of systems engineering prac…
Active Learning with Combinatorial Coverage
Sai Prathyush Katragadda, Tyler Cody, Peter Beling +1
Active learning is a practical field of machine learning that automates the process of selecting which data to label. Current methods are effective in reducing the burden of data l…