2 citations · 4 across the 4 of their papers we have counts for
7 papers
PerfCoder: Large Language Models for Interpretable Code Performance Optimization
Jiuding Yang, Shengyao Lu, Hongxuan Liu +4
Large language models (LLMs) have achieved remarkable progress in automatic code generation, yet their ability to produce high-performance code remains limited--a critical requirem…
Model-Level GNN Explanations via Rule-to-Graph Readout for Logit Reconstruction
Shengyao Lu, Jiuding Yang, Aedan J. DeFrates +3
We propose a novel model-level GNN explanation framework that shifts the explanation target from class-wise rule extraction to rule-based logit reconstruction. Our method recasts t…
TaCIE: Enhancing Instruction Comprehension in Large Language Models through Task-Centred Instruction Evolution
Jiuding Yang, Shengyao Lu, Weidong Guo +4
Large Language Models (LLMs) require precise alignment with complex instructions to optimize their performance in real-world applications. As the demand for refined instruction tun…
EiG-Search: Generating Edge-Induced Subgraphs for GNN Explanation in Linear Time
Shengyao Lu, Bang Liu, Keith G. Mills +2
Understanding and explaining the predictions of Graph Neural Networks (GNNs), is crucial for enhancing their safety and trustworthiness. Subgraph-level explanations are gaining att…
Building Optimal Neural Architectures using Interpretable Knowledge
Keith G. Mills, Fred X. Han, Mohammad Salameh +5
Neural Architecture Search is a costly practice. The fact that a search space can span a vast number of design choices with each architecture evaluation taking nontrivial overhead…
GOAt: Explaining Graph Neural Networks via Graph Output Attribution
Shengyao Lu, Keith G. Mills, Jiao He +2
Understanding the decision-making process of Graph Neural Networks (GNNs) is crucial to their interpretability. Most existing methods for explaining GNNs typically rely on training…