72 citations · 220 across the 23 of their papers we have counts for
23 papers
Local Contrastive Feature learning for Tabular Data
Zhabiz Gharibshah, Xingquan Zhu
Contrastive self-supervised learning has been successfully used in many domains, such as images, texts, graphs, etc., to learn features without requiring label information. In this…
OPR-Miner: Order-preserving rule mining for time series
Youxi Wu, Xiaoqian Zhao, Yan Li +4
Discovering frequent trends in time series is a critical task in data mining. Recently, order-preserving matching was proposed to find all occurrences of a pattern in a time series…
Deep Forest with Hashing Screening and Window Screening
Pengfei Ma, Youxi Wu, Yan Li +4
As a novel deep learning model, gcForest has been widely used in various applications. However, the current multi-grained scanning of gcForest produces many redundant feature vecto…
OPP-Miner: Order-preserving sequential pattern mining
Youxi Wu, Qian Hu, Yan Li +3
A time series is a collection of measurements in chronological order. Discovering patterns from time series is useful in many domains, such as stock analysis, disease detection, an…
GraSSNet: Graph Soft Sensing Neural Networks
Yu Huang, Chao Zhang, Jaswanth Yella +5
In the era of big data, data-driven based classification has become an essential method in smart manufacturing to guide production and optimize inspection. The industrial data obta…
ST-PCNN: Spatio-Temporal Physics-Coupled Neural Networks for Dynamics Forecasting
Yu Huang, James Li, Min Shi +5
Ocean current, fluid mechanics, and many other spatio-temporal physical dynamical systems are essential components of the universe. One key characteristic of such systems is that c…
Physics-Coupled Spatio-Temporal Active Learning for Dynamical Systems
Yu Huang, Yufei Tang, Xingquan Zhu +4
Spatio-temporal forecasting is of great importance in a wide range of dynamical systems applications from atmospheric science, to recent COVID-19 spread modeling. These application…
Predictive Modeling of Hospital Readmission: Challenges and Solutions
Shuwen Wang, Xingquan Zhu
Hospital readmission prediction is a study to learn models from historical medical data to predict probability of a patient returning to hospital in a certain period, 30 or 90 days…
Learning Graph Neural Networks with Positive and Unlabeled Nodes
Man Wu, Shirui Pan, Lan Du +1
Graph neural networks (GNNs) are important tools for transductive learning tasks, such as node classification in graphs, due to their expressive power in capturing complex interdep…
User Response Prediction in Online Advertising
Zhabiz Gharibshah, Xingquan Zhu
Online advertising, as the vast market, has gained significant attention in various platforms ranging from search engines, third-party websites, social media, and mobile apps. The…
TriNE: Network Representation Learning for Tripartite Heterogeneous Networks
Zhabiz Gharibshah, Xingquan Zhu
In this paper, we study network representation learning for tripartite heterogeneous networks which learns node representation features for networks with three types of node entiti…
Evolutionary Architecture Search for Graph Neural Networks
Min Shi, David A. Wilson, Xingquan Zhu +4
Automated machine learning (AutoML) has seen a resurgence in interest with the boom of deep learning over the past decade. In particular, Neural Architecture Search (NAS) has seen…
Topology and Content Co-Alignment Graph Convolutional Learning
Min Shi, Yufei Tang, Xingquan Zhu
In traditional Graph Neural Networks (GNN), graph convolutional learning is carried out through topology-driven recursive node content aggregation for network representation learni…
Multi-Label Graph Convolutional Network Representation Learning
Min Shi, Yufei Tang, Xingquan Zhu +1
Knowledge representation of graph-based systems is fundamental across many disciplines. To date, most existing methods for representation learning primarily focus on networks with…
Feature-Attention Graph Convolutional Networks for Noise Resilient Learning
Min Shi, Yufei Tang, Xingquan Zhu +1
Noise and inconsistency commonly exist in real-world information networks, due to inherent error-prone nature of human or user privacy concerns. To date, tremendous efforts have be…
A Survey and Taxonomy of Adversarial Neural Networks for Text-to-Image Synthesis
Jorge Agnese, Jonathan Herrera, Haicheng Tao +1
Text-to-image synthesis refers to computational methods which translate human written textual descriptions, in the form of keywords or sentences, into images with similar semantic…
Search Efficient Binary Network Embedding
Daokun Zhang, Jie Yin, Xingquan Zhu +1
Traditional network embedding primarily focuses on learning a continuous vector representation for each node, preserving network structure and/or node content information, such tha…
Attributed Network Embedding via Subspace Discovery
Daokun Zhang, Jie Yin, Xingquan Zhu +1
Network embedding aims to learn a latent, low-dimensional vector representations of network nodes, effective in supporting various network analytic tasks. While prior arts on netwo…
SINE: Scalable Incomplete Network Embedding
Daokun Zhang, Jie Yin, Xingquan Zhu +1
Attributed network embedding aims to learn low-dimensional vector representations for nodes in a network, where each node contains rich attributes/features describing node content.…
MetaGraph2Vec: Complex Semantic Path Augmented Heterogeneous Network Embedding
Daokun Zhang, Jie Yin, Xingquan Zhu +1
Network embedding in heterogeneous information networks (HINs) is a challenging task, due to complications of different node types and rich relationships between nodes. As a result…
Network Representation Learning: A Survey
Daokun Zhang, Jie Yin, Xingquan Zhu +1
With the widespread use of information technologies, information networks are becoming increasingly popular to capture complex relationships across various disciplines, such as soc…
Time-Variant Graph Classification
Haishuai Wang
Graphs are commonly used to represent objects, such as images and text, for pattern classification. In a dynamic world, an object may continuously evolve over time, and so does the…
Transfer Learning across Networks for Collective Classification
Meng Fang, Jie Yin, Xingquan Zhu
This paper addresses the problem of transferring useful knowledge from a source network to predict node labels in a newly formed target network. While existing transfer learning re…