69 citations · 327 across the 44 of their papers we have counts for
6 papers · 1 filter
Global Counterfactual Explainer for Graph Neural Networks
Mert Kosan, Zexi Huang, Sourav Medya +2
Graph neural networks (GNNs) find applications in various domains such as computational biology, natural language processing, and computer security. Owing to their popularity, ther…
Mind Reader: Reconstructing complex images from brain activities
Sikun Lin, Thomas Sprague, Ambuj K Singh
Understanding how the brain encodes external stimuli and how these stimuli can be decoded from the measured brain activities are long-standing and challenging questions in neurosci…
Deep Representations for Time-varying Brain Datasets
Sikun Lin, Shuyun Tang, Scott Grafton +1
Finding an appropriate representation of dynamic activities in the brain is crucial for many downstream applications. Due to its highly dynamic nature, temporally averaged fMRI (fu…
Incorporating Heterophily into Graph Neural Networks for Graph Classification
Jiayi Yang, Sourav Medya, Wei Ye
Graph Neural Networks (GNNs) often assume strong homophily for graph classification, seldom considering heterophily, which means connected nodes tend to have different class labels…
Modeling Human-AI Team Decision Making
Wei Ye, Francesco Bullo, Noah Friedkin +1
AI and humans bring complementary skills to group deliberations. Modeling this group decision making is especially challenging when the deliberations include an element of risk and…
Graph Neural Diffusion Networks for Semi-supervised Learning
Wei Ye, Zexi Huang, Yunqi Hong +1
Graph Convolutional Networks (GCN) is a pioneering model for graph-based semi-supervised learning. However, GCN does not perform well on sparsely-labeled graphs. Its two-layer vers…