3 papers
cs.AI2025
Opus: A Quantitative Framework for Workflow Evaluation
Alan Seroul, Théo Fagnoni, Inès Adnani +2
This paper introduces the Opus Workflow Evaluation Framework, a probabilistic-normative formulation for quantifying Workflow quality and efficiency. It integrates notions of correc…
cs.AI2025
Opus: A Prompt Intention Framework for Complex Workflow Generation
Théo Fagnoni, Mahsun Altin, Chia En Chung +4
This paper introduces the Opus Prompt Intention Framework, designed to improve complex Workflow Generation with instruction-tuned Large Language Models (LLMs). We propose an interm…
cs.CV2023
DGM-DR: Domain Generalization with Mutual Information Regularized Diabetic Retinopathy Classification
Aleksandr Matsun, Dana O. Mohamed, Sharon Chokuwa +2
The domain shift between training and testing data presents a significant challenge for training generalizable deep learning models. As a consequence, the performance of models tra…