59 citations · 181 across the 11 of their papers we have counts for
4 papers · 1 filter
Deep Graph Neural Networks with Shallow Subgraph Samplers
Hanqing Zeng, Muhan Zhang, Yinglong Xia +6
While Graph Neural Networks (GNNs) are powerful models for learning representations on graphs, most state-of-the-art models do not have significant accuracy gain beyond two to thre…
Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning
Muhan Zhang, Pan Li, Yinglong Xia +2
In this paper, we provide a theory of using graph neural networks (GNNs) for multi-node representation learning (where we are interested in learning a representation for a set of m…
Deforming the Loss Surface to Affect the Behaviour of the Optimizer
Liangming Chen, Long Jin, Xiujuan Du +2
In deep learning, it is usually assumed that the optimization process is conducted on a shape-fixed loss surface. Differently, we first propose a novel concept of deformation mappi…
Deforming the Loss Surface
Liangming Chen, Long Jin, Xiujuan Du +2
In deep learning, it is usually assumed that the shape of the loss surface is fixed. Differently, a novel concept of deformation operator is first proposed in this paper to deform…