H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 67 Citations 26,044 220 World Ranking 1044 National Ranking 95

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Artificial neural network

Dit-Yan Yeung focuses on Artificial intelligence, Machine learning, Computer vision, Pattern recognition and Deep learning. Benchmark, Eye tracking, Feature, Feature extraction and Convolutional neural network are the core of his Artificial intelligence study. Dit-Yan Yeung has included themes like Network topology and Anomaly detection in his Machine learning study.

His Computer vision study incorporates themes from Embedding and Feature vector. His Pattern recognition research is multidisciplinary, incorporating perspectives in Canopy clustering algorithm, Clustering high-dimensional data, Cluster analysis, Fuzzy clustering and Data stream clustering. His Deep learning research incorporates elements of Artificial neural network and Feature learning.

His most cited work include:

  • Convolutional LSTM Network: a machine learning approach for precipitation nowcasting (1820 citations)
  • Super-resolution through neighbor embedding (1497 citations)
  • Collaborative Deep Learning for Recommender Systems (941 citations)

What are the main themes of his work throughout his whole career to date?

His main research concerns Artificial intelligence, Machine learning, Pattern recognition, Artificial neural network and Deep learning. His research on Artificial intelligence frequently connects to adjacent areas such as Computer vision. The various areas that Dit-Yan Yeung examines in his Machine learning study include Inference and Metric.

The concepts of his Pattern recognition study are interwoven with issues in Facial recognition system and Cluster analysis. His Deep learning research includes themes of Topic model, Convolutional neural network and Bayesian inference. His Kernel embedding of distributions research incorporates themes from Polynomial kernel and Variable kernel density estimation.

He most often published in these fields:

  • Artificial intelligence (80.22%)
  • Machine learning (39.57%)
  • Pattern recognition (27.70%)

What were the highlights of his more recent work (between 2013-2021)?

  • Artificial intelligence (80.22%)
  • Machine learning (39.57%)
  • Deep learning (10.43%)

In recent papers he was focusing on the following fields of study:

Dit-Yan Yeung mainly investigates Artificial intelligence, Machine learning, Deep learning, Artificial neural network and Computer vision. Artificial intelligence and Pattern recognition are commonly linked in his work. His work on Feature learning and Unsupervised learning as part of general Machine learning research is frequently linked to Precipitation and Nowcasting, bridging the gap between disciplines.

His Deep learning research is multidisciplinary, incorporating perspectives in Topic model, Point cloud, Recommender system, State and Bayesian inference. His Overfitting and Supervised learning study in the realm of Artificial neural network connects with subjects such as Bayesian Knowledge Tracing and Student learning. The study incorporates disciplines such as Simultaneous localization and mapping and Leverage in addition to Computer vision.

Between 2013 and 2021, his most popular works were:

  • Convolutional LSTM Network: a machine learning approach for precipitation nowcasting (1820 citations)
  • Collaborative Deep Learning for Recommender Systems (941 citations)
  • Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting (685 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Artificial neural network

Dit-Yan Yeung mainly investigates Artificial intelligence, Machine learning, Deep learning, Benchmark and Convolutional neural network. His Artificial intelligence research is multidisciplinary, incorporating elements of Computer vision and Pattern recognition. Many of his research projects under Computer vision are closely connected to TRECVID with TRECVID, tying the diverse disciplines of science together.

His work on Semi-supervised learning as part of general Machine learning study is frequently linked to Precipitation and Nowcasting, therefore connecting diverse disciplines of science. His research integrates issues of Artificial neural network, Collaborative filtering, Recommender system and Topic model in his study of Deep learning. His Convolutional neural network research integrates issues from Contextual image classification and Frame.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Top Publications

Super-resolution through neighbor embedding

Hong Chang;Dit-Yan Yeung;Yimin Xiong.
computer vision and pattern recognition (2004)

2365 Citations

Convolutional LSTM Network: a machine learning approach for precipitation nowcasting

Xingjian Shi;Zhourong Chen;Hao Wang;Dit-Yan Yeung.
neural information processing systems (2015)

2293 Citations

The Visual Object Tracking VOT2016 Challenge Results

Matej Kristan;Aleš Leonardis;Jiři Matas;Michael Felsberg.
european conference on computer vision (2016)

1423 Citations

Collaborative Deep Learning for Recommender Systems

Hao Wang;Naiyan Wang;Dit-Yan Yeung.
knowledge discovery and data mining (2015)

1223 Citations

Learning a Deep Compact Image Representation for Visual Tracking

Naiyan Wang;Dit-Yan Yeung.
neural information processing systems (2013)

1048 Citations

Constructive algorithms for structure learning in feedforward neural networks for regression problems

Tin-Yau Kwok;Dit-Yan Yeung.
IEEE Transactions on Neural Networks (1997)

630 Citations

SVC2004: First International Signature Verification Competition

Dit-Yan Yeung;Hong Chang;Yimin Xiong;Susan E. George.
Lecture Notes in Computer Science (2004)

542 Citations

Robust path-based spectral clustering

Hong Chang;Dit-Yan Yeung.
Pattern Recognition (2008)

536 Citations

Host-based intrusion detection using dynamic and static behavioral models

Dit-Yan Yeung;Yuxin Ding.
Pattern Recognition (2003)

534 Citations

Mathematical expression recognition: a survey

Kam-Fai Chan;Dit Yan Yeung.
International Journal on Document Analysis and Recognition (2000)

428 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

If you think any of the details on this page are incorrect, let us know.

Contact us

Top Scientists Citing Dit-Yan Yeung

Dacheng Tao

Dacheng Tao

University of Sydney

Publications: 74

Junjun Jiang

Junjun Jiang

Harbin Institute of Technology

Publications: 65

Ming-Hsuan Yang

Ming-Hsuan Yang

University of California, Merced

Publications: 60

Xuelong Li

Xuelong Li

Northwestern Polytechnical University

Publications: 55

Qiang Yang

Qiang Yang

Hong Kong University of Science and Technology

Publications: 48

Philip S. Yu

Philip S. Yu

University of Illinois at Chicago

Publications: 44

Luc Van Gool

Luc Van Gool

ETH Zurich

Publications: 43

Xinbo Gao

Xinbo Gao

Chongqing University of Posts and Telecommunications

Publications: 43

Jiwen Lu

Jiwen Lu

Tsinghua University

Publications: 33

Fatih Porikli

Fatih Porikli

Australian National University

Publications: 30

Qi Tian

Qi Tian

Huawei Technologies (China)

Publications: 30

Huchuan Lu

Huchuan Lu

Dalian University of Technology

Publications: 29

Wangmeng Zuo

Wangmeng Zuo

Harbin Institute of Technology

Publications: 29

Ling Shao

Ling Shao

Inception Institute of Artificial Intelligence

Publications: 29

Liang Lin

Liang Lin

Sun Yat-sen University

Publications: 29

Something went wrong. Please try again later.