41 citations · 47 across the 10 of their papers we have counts for
10 papers
From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisited
Zheng Wang, Hongming Ding, Li Pan +3
Graph-based semi-supervised learning (GSSL) has long been a hot research topic. Traditional methods are generally shallow learners, based on the cluster assumption. Recently, graph…
AIDA: Legal Judgment Predictions for Non-Professional Fact Descriptions via Partial-and-Imbalanced Domain Adaptation
Guangyi Xiao, Xinlong Liu, Hao Chen +2
In this paper, we study the problem of legal domain adaptation problem from an imbalanced source domain to a partial target domain. The task aims to improve legal judgment predicti…
NI-UDA: Graph Adversarial Domain Adaptation from Non-shared-and-Imbalanced Big Data to Small Imbalanced Applications
Guangyi Xiao, Weiwei Xiang, Huan Liu +4
We propose a new general Graph Adversarial Domain Adaptation (GADA) based on semantic knowledge reasoning of class structure for solving the problem of unsupervised domain adaptati…
Recurrent Coupled Topic Modeling over Sequential Documents
Jinjin Guo, Longbing Cao, Zhiguo Gong
The abundant sequential documents such as online archival, social media and news feeds are streamingly updated, where each chunk of documents is incorporated with smoothly evolving…
Zero-shot Node Classification with Decomposed Graph Prototype Network
Zheng Wang, Jialong Wang, Yuchen Guo +1
Node classification is a central task in graph data analysis. Scarce or even no labeled data of emerging classes is a big challenge for existing methods. A natural question arises:…
Expanding Semantic Knowledge for Zero-shot Graph Embedding
Zheng Wang, Ruihang Shao, Changping Wang +3
Zero-shot graph embedding is a major challenge for supervised graph learning. Although a recent method RECT has shown promising performance, its working mechanisms are not clear an…
Micro-supervised Disturbance Learning: A Perspective of Representation Probability Distribution
Jielei Chu, Jing Liu, Hongjun Wang +3
The instability is shown in the existing methods of representation learning based on Euclidean distance under a broad set of conditions. Furthermore, the scarcity and high cost of…
Multi-local Collaborative AutoEncoder
Jielei Chu, Hongjun Wang, Jing Liu +2
The excellent performance of representation learning of autoencoders have attracted considerable interest in various applications. However, the structure and multi-local collaborat…
Unsupervised Feature Learning Architecture with Multi-clustering Integration RBM
Jielei Chu, Hongjun Wang, Jing Liu +2
In this paper, we present a novel unsupervised feature learning architecture, which consists of a multi-clustering integration module and a variant of RBM termed multi-clustering i…
Faster Random Walks By Rewiring Online Social Networks On-The-Fly
Zhuojie Zhou, Nan Zhang, Zhiguo Gong +1
Many online social networks feature restrictive web interfaces which only allow the query of a user's local neighborhood through the interface. To enable analytics over such an onl…