EiG-Search: Generating Edge-Induced Subgraphs for GNN Explanation in Linear Time

1 code implementation • 2 May 2024 • Shengyao Lu, Bang Liu, Keith G. Mills, Jiao He, Di Niu

Understanding and explaining the predictions of Graph Neural Networks (GNNs), is crucial for enhancing their safety and trustworthiness.

Building Optimal Neural Architectures using Interpretable Knowledge

1 code implementation • 20 Mar 2024 • Keith G. Mills, Fred X. Han, Mohammad Salameh, Shengyao Lu, Chunhua Zhou, Jiao He, Fengyu Sun, Di Niu

Neural Architecture Search is a costly practice.

Image Classification Neural Architecture Search

GOAt: Explaining Graph Neural Networks via Graph Output Attribution

1 code implementation • 26 Jan 2024 • Shengyao Lu, Keith G. Mills, Jiao He, Bang Liu, Di Niu

Understanding the decision-making process of Graph Neural Networks (GNNs) is crucial to their interpretability.

Attribute Decision Making

Ternary Singular Value Decomposition as a Better Parameterized Form in Linear Mapping

1 code implementation • 15 Aug 2023 • BoYu Chen, Hanxuan Chen, Jiao He, Fengyu Sun, Shangling Jui

We present a simple yet novel parameterized form of linear mapping to achieves remarkable network compression performance: a pseudo SVD called Ternary SVD (TSVD).

Language Modelling Large Language Model +1