5 citations · 8 across the 3 of their papers we have counts for
3 papers
stat.ML2022★ 5 cited
From Local to Global: Spectral-Inspired Graph Neural Networks
Ningyuan Huang, Soledad Villar, Carey E. Priebe +4
Graph Neural Networks (GNNs) are powerful deep learning methods for Non-Euclidean data. Popular GNNs are message-passing algorithms (MPNNs) that aggregate and combine signals in a…
cs.LG2021★ 1 cited
Global Neighbor Sampling for Mixed CPU-GPU Training on Giant Graphs
Jialin Dong, Da Zheng, Lin F. Yang +1
Graph neural networks (GNNs) are powerful tools for learning from graph data and are widely used in various applications such as social network recommendation, fraud detection, and…
cs.DS2020★ 2 cited
Nearly Linear Row Sampling Algorithm for Quantile Regression
Yi Li, Ruosong Wang, Lin Yang +1
We give a row sampling algorithm for the quantile loss function with sample complexity nearly linear in the dimensionality of the data, improving upon the previous best algorithm w…