54 citations · 59 across the 4 of their papers we have counts for
4 papers
Decoupling the Depth and Scope of Graph Neural Networks
Hanqing Zeng, Muhan Zhang, Yinglong Xia +6
State-of-the-art Graph Neural Networks (GNNs) have limited scalability with respect to the graph and model sizes. On large graphs, increasing the model depth often means exponentia…
Activated Gradients for Deep Neural Networks
Mei Liu, Liangming Chen, Xiaohao Du +2
Deep neural networks often suffer from poor performance or even training failure due to the ill-conditioned problem, the vanishing/exploding gradient problem, and the saddle point…
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…