8 citations · 13 across the 5 of their papers we have counts for
5 papers
Multi-task Self-distillation for Graph-based Semi-Supervised Learning
Yating Ren, Junzhong Ji, Lingfeng Niu +1
Graph convolutional networks have made great progress in graph-based semi-supervised learning. Existing methods mainly assume that nodes connected by graph edges are prone to have…
Latent Network Embedding via Adversarial Auto-encoders
Minglong Lei, Yong Shi, Lingfeng Niu
Graph auto-encoders have proved to be useful in network embedding task. However, current models only consider explicit structures and fail to explore the informative latent structu…
Transformed Regularization for Learning Sparse Deep Neural Networks
Rongrong Ma, Jianyu Miao, Lingfeng Niu +1
Deep neural networks (DNNs) have achieved extraordinary success in numerous areas. However, to attain this success, DNNs often carry a large number of weight parameters, leading to…
A Novel Large-scale Ordinal Regression Model
Yong Shi, Huadong Wang, Xin Shen +1
Ordinal regression (OR) is a special multiclass classification problem where an order relation exists among the labels. Recent years, people share their opinions and sentimental ju…
Diffusion Based Network Embedding
Yong Shi, Minglong Lei, Peng Zhang +1
In network embedding, random walks play a fundamental role in preserving network structures. However, random walk based embedding methods have two limitations. First, random walk m…