1 code implementation • 29 Jul 2022 • Yuxin Ma, Ping Gong, Jun Yi, Zhewei Yao, Cheng Li, Yuxiong He, Feng Yan
We identify the main accuracy impact factors in graph feature quantization and theoretically prove that BiFeat training converges to a network where the loss is within $\epsilon$ of the optimal loss of uncompressed network.
no code implementations • 25 May 2022 • Yili Shen, Xiao Liu, Cheng-Wei Ju, Jiaxu Yan, Jun Yi, Zhou Lin, Hui Guan
Subgraph representation learning based on Graph Neural Network (GNN) has exhibited broad applications in scientific advancements, such as predictions of molecular structure-property relationships and collective cellular function.
no code implementations • 29 Sep 2021 • Syed Zawad, Jun Yi, Minjia Zhang, Cheng Li, Feng Yan, Yuxiong He
Such data heterogeneity and privacy requirements bring unique challenges for learning hyperparameter optimization as the training dynamics change across clients even within the same training round and they are difficult to measure due to privacy constraints.