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 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.
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.
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.
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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)
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)
Discrimination-aware channel pruning for deep neural networks
Zhuangwei Zhuang;Mingkui Tan;Bohan Zhuang;Jing Liu.
neural information processing systems (2018)
Gene selection using hybrid particle swarm optimization and genetic algorithm
Shutao Li;Xixian Wu;Mingkui Tan.
soft computing (2008)
Towards ultrahigh dimensional feature selection for big data
Mingkui Tan;Ivor W. Tsang;Li Wang.
Journal of Machine Learning Research (2014)
Towards Effective Low-Bitwidth Convolutional Neural Networks
Bohan Zhuang;Chunhua Shen;Mingkui Tan;Lingqiao Liu.
computer vision and pattern recognition (2018)
Domain-Symmetric Networks for Adversarial Domain Adaptation
Yabin Zhang;Hui Tang;Kui Jia;Mingkui Tan.
computer vision and pattern recognition (2019)
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)
Graph Convolutional Networks for Temporal Action Localization
Runhao Zeng;Wenbing Huang;Chuang Gan;Mingkui Tan.
international conference on computer vision (2019)
Visual Grounding via Accumulated Attention
Chaorui Deng;Qi Wu;Qingyao Wu;Fuyuan Hu.
computer vision and pattern recognition (2018)
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