Search Results - "Yu, Philip"

Deep Learning for Spatio-Temporal Data Mining: A Survey by Wang, Senzhang, Cao, Jiannong, Yu, Philip

“…With the fast development of various positioning techniques such as Global Position System (GPS), mobile devices and remote sensing, spatio-temporal data has…”
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Graph Self-Supervised Learning: A Survey by Liu, Yixin, Jin, Ming, Pan, Shirui, Zhou, Chuan, Zheng, Yu, Xia, Feng, Yu, Philip S.

“…Deep learning on graphs has attracted significant interests recently. However, most of the works have focused on (semi-) supervised learning, resulting in…”
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A Survey on Knowledge Graphs: Representation, Acquisition, and Applications by Ji, Shaoxiong, Pan, Shirui, Cambria, Erik, Marttinen, Pekka, Yu, Philip S.

“…Human knowledge provides a formal understanding of the world. Knowledge graphs that represent structural relations between entities have become an increasingly…”
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A Comprehensive Survey on Graph Neural Networks by Wu, Zonghan, Pan, Shirui, Chen, Fengwen, Long, Guodong, Zhang, Chengqi, Yu, Philip S.

“…Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and…”
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Generalizing to Unseen Domains: A Survey on Domain Generalization by Wang, Jindong, Lan, Cuiling, Liu, Chang, Ouyang, Yidong, Qin, Tao, Lu, Wang, Chen, Yiqiang, Zeng, Wenjun, Yu, Philip S.

“…Machine learning systems generally assume that the training and testing distributions are the same. To this end, a key requirement is to develop models that…”
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PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning by Wang, Yunbo, Wu, Haixu, Zhang, Jianjin, Gao, Zhifeng, Wang, Jianmin, Yu, Philip S., Long, Mingsheng

“…The predictive learning of spatiotemporal sequences aims to generate future images by learning from the historical context, where the visual dynamics are…”
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A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning by Jin, Di, Yu, Zhizhi, Jiao, Pengfei, Pan, Shirui, He, Dongxiao, Wu, Jia, Yu, Philip S., Zhang, Weixiong

“…Community detection, a fundamental task for network analysis, aims to partition a network into multiple sub-structures to help reveal their latent functions…”
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Heterogeneous Information Network Embedding for Recommendation by Shi, Chuan, Hu, Binbin, Zhao, Wayne Xin, Yu, Philip S.

“…Due to the flexibility in modelling data heterogeneity, heterogeneous information network (HIN) has been adopted to characterize complex and heterogeneous…”
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Multi-View Multi-Label Learning With Sparse Feature Selection for Image Annotation by Zhang, Yongshan, Wu, Jia, Cai, Zhihua, Yu, Philip S.

“…In image analysis, image samples are always represented by multiple view features and associated with multiple class labels for better interpretation. However,…”
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Heterogeneous Graph Propagation Network by Ji, Houye, Wang, Xiao, Shi, Chuan, Wang, Bai, Yu, Philip S.

“…Graph neural network (GNN), as a powerful graph representation technique based on deep learning, has shown superior performance and attracted considerable…”
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Privacy-Preserving Deep Learning Model for Decentralized VANETs Using Fully Homomorphic Encryption and Blockchain by Chen, Jianguo, Li, Kenli, Yu, Philip S.

“…In Vehicular Ad-hoc Networks (VANETs), privacy protection and data security during network transmission and data analysis have attracted attention. In this…”
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Higher-Order Attribute-Enhancing Heterogeneous Graph Neural Networks by Li, Jianxin, Peng, Hao, Cao, Yuwei, Dou, Yingtong, Zhang, Hekai, Yu, Philip S., He, Lifang

“…Graph neural networks (GNNs) have been widely used in deep learning on graphs. They can learn effective node representations that achieve superior performances…”
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A Domain Adaptive Density Clustering Algorithm for Data With Varying Density Distribution by Chen, Jianguo, Yu, Philip S.

“…As one type of efficient unsupervised learning methods, clustering algorithms have been widely used in data mining and knowledge discovery with noticeable…”
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FakeDetector: Effective Fake News Detection with Deep Diffusive Neural Network by Zhang, Jiawei, Dong, Bowen, Yu, Philip S.

“…In recent years, due to the booming development of online social networks, fake news for various commercial and political purposes has been appearing in large…”
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Direction-of-Arrival Estimation Based on Deep Neural Networks With Robustness to Array Imperfections by Liu, Zhang-Meng, Zhang, Chenwei, Yu, Philip S.

“…Lacking of adaptation to various array imperfections is an open problem for most high-precision direction-of-arrival (DOA) estimation methods. Machine…”
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An Integrated Cluster Detection, Optimization, and Interpretation Approach for Financial Data by Li, Tie, Kou, Gang, Peng, Yi, Yu, Philip S.

“…In many financial applications, such as fraud detection, reject inference, and credit evaluation, detecting clusters automatically is critical because it helps…”
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More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence by Zhu, Tianqing, Ye, Dayong, Wang, Wei, Zhou, Wanlei, Yu, Philip S.

“…Artificial Intelligence (AI) has attracted a great deal of attention in recent years. However, alongside all its advancements, problems have also emerged, such…”
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Privacy and Robustness in Federated Learning: Attacks and Defenses by Lyu, Lingjuan, Yu, Han, Ma, Xingjun, Chen, Chen, Sun, Lichao, Zhao, Jun, Yang, Qiang, Yu, Philip S.

“…As data are increasingly being stored in different silos and societies becoming more aware of data privacy issues, the traditional centralized training of…”
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Reinforced, Incremental and Cross-Lingual Event Detection From Social Messages by Peng, Hao, Zhang, Ruitong, Li, Shaoning, Cao, Yuwei, Pan, Shirui, Yu, Philip S.

“…Detecting hot social events (e.g., political scandal, momentous meetings, natural hazards, etc.) from social messages is crucial as it highlights significant…”
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HeteSim: A General Framework for Relevance Measure in Heterogeneous Networks by Chuan Shi, Xiangnan Kong, Yue Huang, Yu, Philip S., Bin Wu

“…Similarity search is an important function in many applications, which usually focuses on measuring the similarity between objects with the same type. However,…”
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