Search Results for author: Laura Freeman

Found 8 papers, 0 papers with code

Test & Evaluation Best Practices for Machine Learning-Enabled Systems

no code implementations10 Oct 2023 Jaganmohan Chandrasekaran, Tyler Cody, Nicola McCarthy, Erin Lanus, Laura Freeman

This report presents best practices for the Test and Evaluation (T&E) of ML-enabled software systems across its lifecycle.

Active Learning with Combinatorial Coverage

no code implementations28 Feb 2023 Sai Prathyush Katragadda, Tyler Cody, Peter Beling, Laura Freeman

The proposed methods are data-centric, as opposed to model-centric, and through our experiments we show that the inclusion of coverage in active learning leads to sampling data that tends to be the best in transferring to better performing models and has a competitive sampling bias compared to benchmark methods.

Active Learning

Rationalization for Explainable NLP: A Survey

no code implementations21 Jan 2023 Sai Gurrapu, Ajay Kulkarni, Lifu Huang, Ismini Lourentzou, Laura Freeman, Feras A. Batarseh

Recent improvements in natural language generation have made rationalization an attractive technique because it is intuitive, human-comprehensible, and accessible to non-technical users.

Explainable Artificial Intelligence (XAI) Question Answering +3

Systematic Training and Testing for Machine Learning Using Combinatorial Interaction Testing

no code implementations28 Jan 2022 Tyler Cody, Erin Lanus, Daniel D. Doyle, Laura Freeman

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.

BIG-bench Machine Learning

A Survey on AI Assurance

no code implementations15 Nov 2021 Feras A. Batarseh, Laura Freeman

Artificial Intelligence (AI) algorithms are increasingly providing decision making and operational support across multiple domains.

Decision Making

Test and Evaluation Framework for Multi-Agent Systems of Autonomous Intelligent Agents

no code implementations25 Jan 2021 Erin Lanus, Ivan Hernandez, Adam Dachowicz, Laura Freeman, Melanie Grande, Andrew Lang, Jitesh H. Panchal, Anthony Patrick, Scott Welch

Test and evaluation is a necessary process for ensuring that engineered systems perform as intended under a variety of conditions, both expected and unexpected.

Investigating the Robustness of Artificial Intelligent Algorithms with Mixture Experiments

no code implementations10 Oct 2020 Jiayi Lian, Laura Freeman, Yili Hong, Xinwei Deng

Artificial intelligent (AI) algorithms, such as deep learning and XGboost, are used in numerous applications including computer vision, autonomous driving, and medical diagnostics.

Autonomous Driving Classification +2

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