H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 32 Citations 4,555 129 World Ranking 7240 National Ranking 687

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Algorithm

Mingkui Tan mainly focuses on Artificial intelligence, Pattern recognition, Feature, Algorithm and Feature learning. His Artificial intelligence study combines topics from a wide range of disciplines, such as Graph and Graph. Many of his research projects under Pattern recognition are closely connected to Domain adaptation with Domain adaptation, tying the diverse disciplines of science together.

Mingkui Tan usually deals with Feature and limits it to topics linked to Feature selection and Dimensionality reduction and Feature model. Mingkui Tan combines subjects such as Selection and Pruning with his study of Algorithm. In his work, Representation, Object detection, Pose, Deep learning and Contextual image classification is strongly intertwined with Segmentation, which is a subfield of Feature learning.

His most cited work include:

  • Deep High-Resolution Representation Learning for Visual Recognition. (244 citations)
  • Discrimination-aware channel pruning for deep neural networks (196 citations)
  • Learning Sparse SVM for Feature Selection on Very High Dimensional Datasets (149 citations)

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

His primary areas of investigation include Artificial intelligence, Machine learning, Pattern recognition, Algorithm and Contextual image classification. His study connects Computer vision and Artificial intelligence. His work on Reinforcement learning is typically connected to Task analysis as part of general Machine learning study, connecting several disciplines of science.

Context is closely connected to Embedding in his research, which is encompassed under the umbrella topic of Pattern recognition. His studies deal with areas such as Inference and Pruning as well as Algorithm. His Feature selection research is multidisciplinary, incorporating elements of Feature extraction and Data mining.

He most often published in these fields:

  • Artificial intelligence (58.24%)
  • Machine learning (23.63%)
  • Pattern recognition (22.53%)

What were the highlights of his more recent work (between 2020-2021)?

  • Artificial intelligence (58.24%)
  • Machine learning (23.63%)
  • Algorithm (19.23%)

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

Mingkui Tan focuses on Artificial intelligence, Machine learning, Algorithm, Benchmark and Pruning. His Artificial intelligence research includes elements of Key and Source code. His studies in Machine learning integrate themes in fields like Metric and Encoding.

His research integrates issues of Matching, Structure and Generative model in his study of Benchmark. His studies examine the connections between Pruning and genetics, as well as such issues in Compression, with regards to Quantization. His Contextual image classification study which covers Discriminative model that intersects with Kernel.

Between 2020 and 2021, his most popular works were:

  • A Real-Time Action Representation With Temporal Encoding and Deep Compression (6 citations)
  • Online Adaptive Asymmetric Active Learning With Limited Budgets (6 citations)
  • Learning Sparse PCA with Stabilized ADMM Method on Stiefel Manifold (2 citations)

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

  • Artificial intelligence
  • Machine learning
  • Algorithm

His main research concerns Artificial intelligence, Feature extraction, Machine learning, Key and Paragraph. Mingkui Tan incorporates Artificial intelligence and Focus in his research. The Feature extraction study combines topics in areas such as Representation, Inference, Convolutional neural network and Encoding.

Mingkui Tan has researched Machine learning in several fields, including Measure and Anomaly detection. His Key investigation overlaps with Graph, Closed captioning, Natural language processing, Paraphrase and Construct. Paragraph is intertwined with Fluent, Code, Intelligent agent, Information retrieval and Knowledge graph in his study.

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.

Best Publications

Deep High-Resolution Representation Learning for Visual Recognition.

Jingdong Wang;Ke Sun;Tianheng Cheng;Borui Jiang.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2020)

244 Citations

Learning Sparse SVM for Feature Selection on Very High Dimensional Datasets

Mingkui Tan;Li Wang;Li Wang;Ivor W. Tsang.
international conference on machine learning (2010)

206 Citations

Discrimination-aware channel pruning for deep neural networks

Zhuangwei Zhuang;Mingkui Tan;Bohan Zhuang;Jing Liu.
neural information processing systems (2018)

196 Citations

Gene selection using hybrid particle swarm optimization and genetic algorithm

Shutao Li;Xixian Wu;Mingkui Tan.
soft computing (2008)

174 Citations

Towards ultrahigh dimensional feature selection for big data

Mingkui Tan;Ivor W. Tsang;Li Wang.
Journal of Machine Learning Research (2014)

166 Citations

Towards Effective Low-Bitwidth Convolutional Neural Networks

Bohan Zhuang;Chunhua Shen;Mingkui Tan;Lingqiao Liu.
computer vision and pattern recognition (2018)

143 Citations

Domain-Symmetric Networks for Adversarial Domain Adaptation

Yabin Zhang;Hui Tang;Kui Jia;Mingkui Tan.
computer vision and pattern recognition (2019)

130 Citations

Heterogeneous Domain Adaptation for Multiple Classes

Joey Tianyi Zhou;Ivor W. Tsang;Sinno Jialin Pan;Mingkui Tan.
international conference on artificial intelligence and statistics (2014)

126 Citations

Graph Convolutional Networks for Temporal Action Localization

Runhao Zeng;Wenbing Huang;Chuang Gan;Mingkui Tan.
international conference on computer vision (2019)

124 Citations

Visual Grounding via Accumulated Attention

Chaorui Deng;Qi Wu;Qingyao Wu;Fuyuan Hu.
computer vision and pattern recognition (2018)

120 Citations

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Best Scientists Citing Mingkui Tan

Dacheng Tao

Dacheng Tao

University of Sydney

Publications: 30

Yanzhi Wang

Yanzhi Wang

Northeastern University

Publications: 30

Yi Yang

Yi Yang

Zhejiang University

Publications: 28

Chunhua Shen

Chunhua Shen

University of Adelaide

Publications: 26

Lei Zhang

Lei Zhang

Hong Kong Polytechnic University

Publications: 23

Ivor W. Tsang

Ivor W. Tsang

University of Technology Sydney

Publications: 22

Lina Yao

Lina Yao

UNSW Sydney

Publications: 21

Huazhu Fu

Huazhu Fu

Agency for Science, Technology and Research

Publications: 19

Jingdong Wang

Jingdong Wang

Microsoft (United States)

Publications: 18

Bernard Ghanem

Bernard Ghanem

King Abdullah University of Science and Technology

Publications: 18

Yanning Zhang

Yanning Zhang

Northwestern Polytechnical University

Publications: 18

Baochang Zhang

Baochang Zhang

Beihang University

Publications: 17

Chang Xu

Chang Xu

University of Sydney

Publications: 17

Qi Tian

Qi Tian

Huawei Technologies (China)

Publications: 17

Xi Peng

Xi Peng

Sichuan University

Publications: 17

Bin Ren

Bin Ren

Xiamen University

Publications: 16

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