3 papers
cs.HC2026
People Can Accurately Predict Behavior of Complex Algorithms That Are Available, Compact, and Aligned
Lindsay Popowski, Helena Vasconcelos, Ignacio Javier Fernandez +4
Users trust algorithms more when they can predict the algorithms' behavior. Simple algorithms trivially yield predictively accurate mental models, but modern AI algorithms have oft…
cs.LG2024
BALI: Learning Neural Networks via Bayesian Layerwise Inference
Richard Kurle, Alexej Klushyn, Ralf Herbrich
We introduce a new method for learning Bayesian neural networks, treating them as a stack of multivariate Bayesian linear regression models. The main idea is to infer the layerwise…
cs.CL2024
Learning to Predict Usage Options of Product Reviews with LLM-Generated Labels
Leo Kohlenberg, Leonard Horns, Frederic Sadrieh +7
Annotating large datasets can be challenging. However, crowd-sourcing is often expensive and can lack quality, especially for non-trivial tasks. We propose a method of using LLMs a…