3 papers
cs.LG2026
Modular Multimodal Classification Without Fine-Tuning: A Simple Compositional Approach
Herman Bergström, Aditya Mehrotra, Rahul G. Krishnan
We introduce CoMET, \textit{\textbf{C}omposing \textbf{M}odality \textbf{E}ncoders with \textbf{T}abular foundation models}, a simple yet highly competitive method for multimodal c…
cs.CV2025
When are radiology reports useful for training medical image classifiers?
Herman Bergström, Zhongqi Yue, Fredrik D. Johansson
Medical images used to train machine learning models are often accompanied by radiology reports containing rich expert annotations. However, relying on these reports as inputs for…
cs.LG2024
Active Preference Learning for Ordering Items In- and Out-of-sample
Herman Bergström, Emil Carlsson, Devdatt Dubhashi +1
Learning an ordering of items based on pairwise comparisons is useful when items are difficult to rate consistently on an absolute scale, for example, when annotators have to make…