Nowcasting and Precipitation are fields of study that overlap with his Meteorology research. He merges many fields, such as Precipitation and Meteorology, in his writings. Dit-Yan Yeung undertakes interdisciplinary study in the fields of Artificial intelligence and Probabilistic logic through his works. He incorporates Machine learning and Mathematical optimization in his studies. By researching both Mathematical optimization and Machine learning, he produces research that crosses academic boundaries. His Algorithm study frequently draws connections to other fields, such as State (computer science). His research links Algorithm with State (computer science). In his works, he performs multidisciplinary study on Programming language and Operating system. As part of his studies on Operating system, he often connects relevant areas like Constructive.
His Artificial intelligence study frequently draws parallels with other fields, such as Pattern recognition (psychology), Image (mathematics), Probabilistic logic, Deep learning, Artificial neural network and Bayesian probability. Many of his studies involve connections with topics such as Deep learning and Artificial neural network and Machine learning. Dit-Yan Yeung integrates several fields in his works, including Algorithm and Artificial intelligence. Dit-Yan Yeung merges Statistics with Machine learning in his study.
Dit-Yan Yeung performs multidisciplinary study in the fields of Artificial intelligence and Cognitive psychology via his papers. Dit-Yan Yeung combines Cognitive psychology and Artificial intelligence in his studies. He undertakes interdisciplinary study in the fields of Machine learning and Bayesian network through his research. While working on this project, he studies both Bayesian network and Bayesian inference. In his study, he carries out multidisciplinary Operating system and Tracing research. Dit-Yan Yeung merges many fields, such as Deep learning and Graphical model, in his writings. Dit-Yan Yeung conducts interdisciplinary study in the fields of Graphical model and Deep learning through his research. Probabilistic logic and Inference are two areas of study in which Dit-Yan Yeung engages in interdisciplinary work. With his scientific publications, his incorporates both Inference and Probabilistic logic.
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Convolutional LSTM Network: a machine learning approach for precipitation nowcasting
Xingjian Shi;Zhourong Chen;Hao Wang;Dit-Yan Yeung.
neural information processing systems (2015)
Super-resolution through neighbor embedding
Hong Chang;Dit-Yan Yeung;Yimin Xiong.
computer vision and pattern recognition (2004)
The Visual Object Tracking VOT2016 Challenge Results
Matej Kristan;Aleš Leonardis;Jiři Matas;Michael Felsberg.
european conference on computer vision (2016)
Collaborative Deep Learning for Recommender Systems
Hao Wang;Naiyan Wang;Dit-Yan Yeung.
knowledge discovery and data mining (2015)
Learning a Deep Compact Image Representation for Visual Tracking
Naiyan Wang;Dit-Yan Yeung.
neural information processing systems (2013)
Constructive algorithms for structure learning in feedforward neural networks for regression problems
Tin-Yau Kwok;Dit-Yan Yeung.
IEEE Transactions on Neural Networks (1997)
Robust path-based spectral clustering
Hong Chang;Dit-Yan Yeung.
Pattern Recognition (2008)
Host-based intrusion detection using dynamic and static behavioral models
Dit-Yan Yeung;Yuxin Ding.
Pattern Recognition (2003)
SVC2004: First International Signature Verification Competition
Dit-Yan Yeung;Hong Chang;Yimin Xiong;Susan E. George.
Lecture Notes in Computer Science (2004)
Human Action Recognition Using Factorized Spatio-Temporal Convolutional Networks
Lin Sun;Kui Jia;Dit-Yan Yeung;Bertram E. Shi.
international conference on computer vision (2015)
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