3 citations · 4 across the 3 of their papers we have counts for
3 papers
cs.LG2024★ 3 cited
GraphScale: A Framework to Enable Machine Learning over Billion-node Graphs
Vipul Gupta, Xin Chen, Ruoyun Huang +3
Graph Neural Networks (GNNs) have emerged as powerful tools for supervised machine learning over graph-structured data, while sampling-based node representation learning is widely…
stat.ML2024
Sharpness-diversity tradeoff: improving flat ensembles with SharpBalance
Haiquan Lu, Xiaotian Liu, Yefan Zhou +6
Recent studies on deep ensembles have identified the sharpness of the local minima of individual learners and the diversity of the ensemble members as key factors in improving test…
cs.LG2024★ 1 cited
Enhancing Size Generalization in Graph Neural Networks through Disentangled Representation Learning
Zheng Huang, Qihui Yang, Dawei Zhou +1
Although most graph neural networks (GNNs) can operate on graphs of any size, their classification performance often declines on graphs larger than those encountered during trainin…