Publications (14)
Measuring AI Systems Beyond Accuracy
Violet Turri, Rachel Dzombak, Eric Heim +3
Current test and evaluation (T&E) methods for assessing machine learning (ML) system performance often rely on incomplete metrics. Testing is additionally often siloed from the oth…
Efficient Online Relative Comparison Kernel Learning
Eric Heim, Matthew Berger, Lee M. Seversky +1
Learning a kernel matrix from relative comparison human feedback is an important problem with applications in collaborative filtering, object retrieval, and search. For learning a…
Active Perceptual Similarity Modeling with Auxiliary Information
Eric Heim, Matthew Berger, Lee Seversky +1
Learning a model of perceptual similarity from a collection of objects is a fundamental task in machine learning underlying numerous applications. A common way to learn such a mode…
A Decision-driven Methodology for Designing Uncertainty-aware AI Self-Assessment
Gregory Canal, Vladimir Leung, Philip Sage +2
Artificial intelligence (AI) has revolutionized decision-making processes and systems throughout society and, in particular, has emerged as a significant technology in high-impact…
Generating Triples with Adversarial Networks for Scene Graph Construction
Matthew Klawonn, Eric Heim
Driven by successes in deep learning, computer vision research has begun to move beyond object detection and image classification to more sophisticated tasks like image captioning…
Factor Analysis on Citation, Using a Combined Latent and Logistic Regression Model
Namjoon Suh, Xiaoming Huo, Eric Heim +1
We propose a combined model, which integrates the latent factor model and the logistic regression model, for the citation network. It is noticed that neither a latent factor model…