D-Index & Metrics Best Publications
Research.com 2023 Rising Star of Science Award Badge

D-Index & Metrics D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines.

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 30 Citations 6,224 85 World Ranking 10051 National Ranking 608
Rising Stars D-index 30 Citations 6,261 88 World Ranking 987 National Ranking 40

Research.com Recognitions

Awards & Achievements

2023 - Research.com Rising Star of Science Award

Overview

What is he best known for?

The fields of study Yongxin Yang is best known for:

  • Gene
  • Cancer
  • Amino acid

Yongxin Yang works mostly in the field of Systems engineering, limiting it down to concerns involving Task (project management) and, occasionally, Management and Multi-task learning. His Task (project management) research extends to Management, which is thematically connected. His research on Artificial intelligence frequently connects to adjacent areas such as Training set. Machine learning and Feature learning are two areas of study in which he engages in interdisciplinary research. Yongxin Yang undertakes multidisciplinary investigations into Biochemistry and Food science in his work. He connects Food science with Biochemistry in his research. He performs multidisciplinary study on Deep learning and Convolutional neural network in his works. Yongxin Yang undertakes multidisciplinary studies into Convolutional neural network and Deep learning in his work. His work often combines Gene and Proteome studies.

His most cited work include:

  • Sketch-a-Net: A Deep Neural Network that Beats Humans (225 citations)
  • TAT-modified nanosilver for combating multidrug-resistant cancer (167 citations)
  • In vitro and in vivo evaluation of actively targetable nanoparticles for paclitaxel delivery (135 citations)

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

His study focuses on the intersection of Flutter and fields such as Aerodynamics with connections in the field of Aerospace engineering. He connects Aerospace engineering with Aerodynamics in his study. He combines topics linked to Pattern recognition (psychology), Artificial neural network and Deep learning with his work on Artificial intelligence. His Pattern recognition (psychology) study frequently draws connections between adjacent fields such as Artificial intelligence. Yongxin Yang undertakes interdisciplinary study in the fields of Artificial neural network and Machine learning through his research. His study deals with a combination of Machine learning and Deep learning. In his study, Yongxin Yang carries out multidisciplinary Biochemistry and Gene research. Yongxin Yang incorporates Gene and Biochemistry in his research. Yongxin Yang performs multidisciplinary studies into Structural engineering and Girder in his work.

Yongxin Yang most often published in these fields:

  • Artificial intelligence (39.06%)
  • Machine learning (29.69%)
  • Biochemistry (27.34%)

What were the highlights of his more recent work (between 2020-2022)?

  • Artificial intelligence (42.86%)
  • Pattern recognition (psychology) (35.71%)
  • Set (abstract data type) (28.57%)

In recent works Yongxin Yang was focusing on the following fields of study:

As part of the same scientific family, Yongxin Yang usually focuses on Tree (set theory), concentrating on Mathematical analysis and intersecting with Domain (mathematical analysis) and Generalization. In his works, he conducts interdisciplinary research on Domain (mathematical analysis) and Mathematical analysis. In his research, Yongxin Yang performs multidisciplinary study on Generalization and Statistics. His work is dedicated to discovering how Matching (statistics), Statistics are connected with Generalizability theory and other disciplines. His study explores the link between Benchmark (surveying) and topics such as Geodesy that cross with problems in Grid. His research on Grid often connects related topics like Geodesy. He works mostly in the field of Block (permutation group theory), limiting it down to topics relating to Geometry and, in certain cases, Constraint (computer-aided design). Many of his studies on Constraint (computer-aided design) apply to Geometry as well. He is involved in relevant fields of research such as Raman spectroscopy and Adaptation (eye) in the realm of Optics.

Between 2020 and 2022, his most popular works were:

  • Learning Generalisable Omni-Scale Representations for Person Re-Identification (56 citations)
  • Domain Adaptive Ensemble Learning (54 citations)
  • Domain Generalization with MixStyle (36 citations)

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

  • Cobb angle
  • Scoliosis
  • Machine learning

His Mathematical analysis study frequently draws parallels with other fields, such as Domain (mathematical analysis) and Generalization. Yongxin Yang combines Domain (mathematical analysis) and Mathematical analysis in his research. Yongxin Yang undertakes multidisciplinary investigations into Generalization and Statistics in his work. His Generalizability theory research extends to Statistics, which is thematically connected. Yongxin Yang bridges between several scientific fields such as Mixing (physics) and Scale (ratio) in his study of Quantum mechanics. His Scale (ratio) study frequently links to related topics such as Quantum mechanics. His research combines Artificial intelligence and Pattern recognition (psychology). His study connects Ensemble learning and Artificial intelligence. In his articles, he combines various disciplines, including Machine learning and Discriminative model.

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

Learning to Compare: Relation Network for Few-Shot Learning

Flood Sung;Yongxin Yang;Li Zhang;Tao Xiang.
computer vision and pattern recognition (2018)

2148 Citations

Deeper, Broader and Artier Domain Generalization

Da Li;Yongxin Yang;Yi-Zhe Song;Timothy M. Hospedales.
international conference on computer vision (2017)

498 Citations

Learning to Generalize: Meta-Learning for Domain Generalization

Da Li;Yongxin Yang;Yi-Zhe Song;Timothy M. Hospedales.
national conference on artificial intelligence (2018)

364 Citations

Omni-Scale Feature Learning for Person Re-Identification

Kaiyang Zhou;Yongxin Yang;Andrea Cavallaro;Tao Xiang.
international conference on computer vision (2019)

355 Citations

When Face Recognition Meets with Deep Learning: An Evaluation of Convolutional Neural Networks for Face Recognition

Guosheng Hu;Yongxin Yang;Dong Yi;Josef Kittler.
international conference on computer vision (2015)

275 Citations

Sketch-a-Net: A Deep Neural Network that Beats Humans

Qian Yu;Yongxin Yang;Feng Liu;Yi-Zhe Song.
International Journal of Computer Vision (2017)

238 Citations

Episodic Training for Domain Generalization

Da Li;Jianshu Zhang;Yongxin Yang;Cong Liu.
international conference on computer vision (2019)

213 Citations

Sketch-a-Net that Beats Humans

Qian Yu;Yongxin Yang;Yi-Zhe Song;Tao Xiang.
british machine vision conference (2015)

197 Citations

Deep Multi-task Representation Learning: A Tensor Factorisation Approach

Yongxin Yang;Timothy M. Hospedales.
international conference on learning representations (2017)

136 Citations

Actor-Critic Sequence Training for Image Captioning.

Li Zhang;Flood Sung;Feng Liu;Tao Xiang.
arXiv: Computer Vision and Pattern Recognition (2017)

118 Citations

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Best Scientists Citing Yongxin Yang

Tao Xiang

Tao Xiang

University of Surrey

Publications: 60

Timothy M. Hospedales

Timothy M. Hospedales

University of Edinburgh

Publications: 54

Yi-Zhe Song

Yi-Zhe Song

University of Surrey

Publications: 44

Yanwei Fu

Yanwei Fu

Fudan University

Publications: 36

Ling Shao

Ling Shao

Terminus International

Publications: 34

Barbara Caputo

Barbara Caputo

Polytechnic University of Turin

Publications: 29

Shaogang Gong

Shaogang Gong

Queen Mary University of London

Publications: 24

Chelsea Finn

Chelsea Finn

Stanford University

Publications: 24

Yu Zhang

Yu Zhang

Southern University of Science and Technology

Publications: 21

Luc Van Gool

Luc Van Gool

ETH Zurich

Publications: 21

Yu-Gang Jiang

Yu-Gang Jiang

Fudan University

Publications: 20

Wenjun Zeng

Wenjun Zeng

Microsoft (United States)

Publications: 20

Qi Tian

Qi Tian

Huawei Technologies (China)

Publications: 19

Jiebo Luo

Jiebo Luo

University of Rochester

Publications: 19

Dacheng Tao

Dacheng Tao

University of Sydney

Publications: 18

Pheng-Ann Heng

Pheng-Ann Heng

Chinese University of Hong Kong

Publications: 17

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