19 citations · 19 across the 5 of their papers we have counts for
5 papers
FDN: Interpretable Spatiotemporal Forecasting with Future Decomposition Networks
Nicholas Majeske, Ariful Azad
Spatiotemporal systems comprise a collection of spatially distributed yet interdependent entities each generating unique dynamic signals. Highly sophisticated methods have been pro…
Shapley-Value-Based Graph Sparsification for GNN Inference
Selahattin Akkas, Ariful Azad
Graph sparsification is a key technique for improving inference efficiency in Graph Neural Networks by removing edges with minimal impact on predictions. GNN explainability methods…
DistShap: Scalable GNN Explanations with Distributed Shapley Values
Selahattin Akkas, Aditya Devarakonda, Ariful Azad
With the growing adoption of graph neural networks (GNNs), explaining their predictions has become increasingly important. However, attributing predictions to specific edges or fea…
GNNShap: Scalable and Accurate GNN Explanation using Shapley Values
Selahattin Akkas, Ariful Azad
Graph neural networks (GNNs) are popular machine learning models for graphs with many applications across scientific domains. However, GNNs are considered black box models, and it…
Contrastive Learning Improves Critical Event Prediction in COVID-19 Patients
Tingyi Wanyan, Hossein Honarvar, Suraj K. Jaladanki +13
Machine Learning (ML) models typically require large-scale, balanced training data to be robust, generalizable, and effective in the context of healthcare. This has been a major is…