5 papers
LLM-powered reasoning in agent-based modeling
Sifat Afroj Moon, Dakotah Maguire, Adam Spannaus +5
Agent-based modeling (ABM) has the capability to model millions of individuals and their interactions, which is useful for policy making. However, ABMs have traditionally relied on…
In-Domain Supervised Pathology Report Classification: A Reproducible Pipeline from Data Curation to Production-Matched Evaluation
Isaac Hands, Bin Huang, Adam Spannaus +4
We introduce an in-domain supervised pipeline designed to counter the out-of-distribution performance drop that hampers supervised biomedical NLP models, a problem observed when mo…
Data Assimilation for Robust UQ Within Agent-Based Simulation on HPC Systems
Adam Spannaus, Sifat Afroj Moon, John Gounley +1
Agent-based simulation provides a powerful tool for in silico system modeling. However, these simulations do not provide built-in methods for uncertainty quantification (UQ). Withi…
Global explainability of a deep abstaining classifier
Sayera Dhaubhadel, Jamaludin Mohd-Yusof, Benjamin H. McMahon +8
We present a global explainability method to characterize sources of errors in the histology prediction task of our real-world multitask convolutional neural network (MTCNN)-based…
Topological Interpretability for Deep-Learning
Adam Spannaus, Heidi A. Hanson, Lynne Penberthy +1
With the growing adoption of AI-based systems across everyday life, the need to understand their decision-making mechanisms is correspondingly increasing. The level at which we can…