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Found 3 papers, 1 papers with code
Exploiting Inter-sample and Inter-feature Relations in Dataset Distillation
However, these methods face two primary limitations: the dispersed feature distribution within the same class in synthetic datasets, reducing class discrimination, and an exclusive focus on mean feature consistency, lacking precision and comprehensiveness.
The Expressive Power of Graph Neural Networks: A Survey
Graph neural networks (GNNs) are effective machine learning models for many graph-related applications.
Efficient Learning for Selecting Top-m Context-Dependent Designs
We consider a simulation optimization problem for a context-dependent decision-making, which aims to determine the top-m designs for all contexts.
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