27 citations · 40 across the 13 of their papers we have counts for
5 papers · 1 filter
Neighborhood Convolutional Network: A New Paradigm of Graph Neural Networks for Node Classification
Jinsong Chen, Boyu Li, Kun He
The decoupled Graph Convolutional Network (GCN), a recent development of GCN that decouples the neighborhood aggregation and feature transformation in each convolutional layer, has…
Adversarially Robust Generalization Just Requires More Unlabeled Data
Runtian Zhai, Tianle Cai, Di He +4
Neural network robustness has recently been highlighted by the existence of adversarial examples. Many previous works show that the learned networks do not perform well on perturbe…
A Learning based Branch and Bound for Maximum Common Subgraph Problems
Yan-li Liu, Chu-min Li, Hua Jiang +1
Branch-and-bound (BnB) algorithms are widely used to solve combinatorial problems, and the performance crucially depends on its branching heuristic.In this work, we consider a typi…
Towards Understanding Learning Representations: To What Extent Do Different Neural Networks Learn the Same Representation
Liwei Wang, Lunjia Hu, Jiayuan Gu +4
It is widely believed that learning good representations is one of the main reasons for the success of deep neural networks. Although highly intuitive, there is a lack of theory an…
Improving the Generalization of Adversarial Training with Domain Adaptation
Chuanbiao Song, Kun He, Liwei Wang +1
By injecting adversarial examples into training data, adversarial training is promising for improving the robustness of deep learning models. However, most existing adversarial tra…