Browsing Department of Computational and Data Sciences (CDS) by Subject "Artificial Intelligence"
Now showing items 1-2 of 2
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Methods for Improving Data-efficiency and Trustworthiness using Natural Language Supervision
Traditional strategies to build machine learning based classification systems employ discrete labels as targets. This limits the usefulness of such systems in two ways. First, the generalizability of these systems is limited ... -
Unsupervised Test-time Adaptation for Patient-Specific Deep Learning Models in Medical Imaging
Deep learning (DL) models have achieved state-of-the-art results in multiple medical imaging applications, resulting in the widespread adoption of artificial intelligence (AI) models for radiological workflows. Despite ...