1 citations · 1 across the 5 of their papers we have counts for
9 papers
On The Expressive Power of GNN Derivatives
Yam Eitan, Moshe Eliasof, Yoav Gelberg +3
Despite significant advances in Graph Neural Networks (GNNs), their limited expressivity remains a fundamental challenge. Research on GNN expressivity has produced many expressive…
Beyond Token Probes: Hallucination Detection via Activation Tensors with ACT-ViT
Guy Bar-Shalom, Fabrizio Frasca, Yaniv Galron +2
Detecting hallucinations in Large Language Model-generated text is crucial for their safe deployment. While probing classifiers show promise, they operate on isolated layer-token p…
GradMetaNet: An Equivariant Architecture for Learning on Gradients
Yoav Gelberg, Yam Eitan, Aviv Navon +5
Gradients of neural networks encode valuable information for optimization, editing, and analysis of models. Therefore, practitioners often treat gradients as inputs to task-specifi…
Understanding and Improving Laplacian Positional Encodings For Temporal GNNs
Yaniv Galron, Fabrizio Frasca, Haggai Maron +2
Temporal graph learning has applications in recommendation systems, traffic forecasting, and social network analysis. Although multiple architectures have been introduced, progress…
It Takes a Graph to Know a Graph: Rewiring for Homophily with a Reference Graph
Harel Mendelman, Haggai Maron, Ronen Talmon
Graph Neural Networks (GNNs) excel at analyzing graph-structured data but struggle on heterophilic graphs, where connected nodes often belong to different classes. While this chall…
Efficient GNN Training Through Structure-Aware Randomized Mini-Batching
Vignesh Balaji, Christos Kozyrakis, Gal Chechik +1
Graph Neural Networks (GNNs) enable learning on realworld graphs and mini-batch training has emerged as the de facto standard for training GNNs because it can scale to very large g…