8 citations · 27 across the 17 of their papers we have counts for
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cs.LG2026
xHC: Expanded Hyper-Connections
Xiangdong Zhang, Xiaohan Qin, Sunan Zou +10
Hyper-Connections (HC) expand the residual stream of Transformers into parallel streams, providing a form of memory scaling beyond model width and depth. Manifold-Constrained H…
cs.LG2022★ 6 cited
Localized Contrastive Learning on Graphs
Hengrui Zhang, Qitian Wu, Yu Wang +3
Contrastive learning methods based on InfoNCE loss are popular in node representation learning tasks on graph-structured data. However, its reliance on data augmentation and its qu…
cs.LG2022★ 4 cited
DOTIN: Dropping Task-Irrelevant Nodes for GNNs
Shaofeng Zhang, Feng Zhu, Junchi Yan +2
Scalability is an important consideration for deep graph neural networks. Inspired by the conventional pooling layers in CNNs, many recent graph learning approaches have introduced…