4 citations · 6 across the 3 of their papers we have counts for
3 papers
cs.CV2024
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…
cs.LG2024★ 2 cited
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…
cs.LG2023★ 4 cited
A General-Purpose Transferable Predictor for Neural Architecture Search
Fred X. Han, Keith G. Mills, Fabian Chudak +6
Understanding and modelling the performance of neural architectures is key to Neural Architecture Search (NAS). Performance predictors have seen widespread use in low-cost NAS and…