64 citations · 78 across the 8 of their papers we have counts for
8 papers
Entropy Neural Estimation for Graph Contrastive Learning
Yixuan Ma, Xiaolin Zhang, Peng Zhang +1
Contrastive learning on graphs aims at extracting distinguishable high-level representations of nodes. In this paper, we theoretically illustrate that the entropy of a dataset can…
GIPA: A General Information Propagation Algorithm for Graph Learning
Houyi Li, Zhihong Chen, Zhao Li +3
Graph neural networks (GNNs) have been widely used in graph-structured data computation, showing promising performance in various applications such as node classification, link pre…
DyStyle: Dynamic Neural Network for Multi-Attribute-Conditioned Style Editing
Bingchuan Li, Shaofei Cai, Wei Liu +4
The semantic controllability of StyleGAN is enhanced by unremitting research. Although the existing weak supervision methods work well in manipulating the style codes along one att…
GIPA: General Information Propagation Algorithm for Graph Learning
Qinkai Zheng, Houyi Li, Peng Zhang +4
Graph neural networks (GNNs) have been popularly used in analyzing graph-structured data, showing promising results in various applications such as node classification, link predic…
GraphTheta: A Distributed Graph Neural Network Learning System With Flexible Training Strategy
Yongchao Liu, Houyi Li, Guowei Zhang +8
Graph neural networks (GNNs) have been demonstrated as a powerful tool for analyzing non-Euclidean graph data. However, the lack of efficient distributed graph learning systems sev…
TensorCoder: Dimension-Wise Attention via Tensor Representation for Natural Language Modeling
Shuai Zhang, Peng Zhang, Xindian Ma +3
Transformer has been widely-used in many Natural Language Processing (NLP) tasks and the scaled dot-product attention between tokens is a core module of Transformer. This attention…
Revisiting Regex Generation for Modeling Industrial Applications by Incorporating Byte Pair Encoder
Desheng Wang, Jiawei Liu, Xiang Qi +2
Regular expression is important for many natural language processing tasks especially when used to deal with unstructured and semi-structured data. This work focuses on automatical…
Encoding word order in complex embeddings
Benyou Wang, Donghao Zhao, Christina Lioma +3
Sequential word order is important when processing text. Currently, neural networks (NNs) address this by modeling word position using position embeddings. The problem is that posi…