H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 78 Citations 19,396 302 World Ranking 499 National Ranking 40

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Statistics

Rong Jin mainly focuses on Artificial intelligence, Machine learning, Data mining, Information retrieval and Pattern recognition. Artificial intelligence and Optimization problem are commonly linked in his work. His study in the fields of Support vector machine under the domain of Machine learning overlaps with other disciplines such as Set.

Rong Jin interconnects Contextual image classification, Learning to rank and Collaborative filtering in the investigation of issues within Data mining. His Information retrieval research includes themes of Language model, Visual Word and Text mining. His Pattern recognition research is multidisciplinary, incorporating elements of Histogram, Cognitive neuroscience of visual object recognition and Categorization.

His most cited work include:

  • Understanding bag-of-words model: A statistical framework (394 citations)
  • Batch mode active learning and its application to medical image classification (319 citations)
  • Combining link and content for community detection: a discriminative approach (305 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, Mathematical optimization, Pattern recognition and Information retrieval. His research in Artificial intelligence tackles topics such as Data mining which are related to areas like Collaborative filtering. As part of his studies on Machine learning, Rong Jin often connects relevant subjects like Empirical research.

As a member of one scientific family, Rong Jin mostly works in the field of Mathematical optimization, focusing on Rate of convergence and, on occasion, Stochastic optimization. His study in Kernel method, Feature extraction and Kernel are all subfields of Pattern recognition. His study in the field of Human–computer information retrieval also crosses realms of Set.

He most often published in these fields:

  • Artificial intelligence (51.65%)
  • Machine learning (29.23%)
  • Mathematical optimization (18.46%)

What were the highlights of his more recent work (between 2018-2021)?

  • Artificial intelligence (51.65%)
  • Mathematical optimization (18.46%)
  • Deep learning (3.96%)

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

Rong Jin mainly investigates Artificial intelligence, Mathematical optimization, Deep learning, Machine learning and Computer vision. His study in Artificial intelligence concentrates on Softmax function, Image, Feature learning, Artificial neural network and Contextual image classification. The study incorporates disciplines such as E-commerce and Ranking, Information retrieval, Relevance in addition to Feature learning.

His study looks at the intersection of Mathematical optimization and topics like Rate of convergence with Non convex optimization. His Deep learning research is multidisciplinary, relying on both Algorithm and Benchmark. With his scientific publications, his incorporates both Machine learning and Scheme.

Between 2018 and 2021, his most popular works were:

  • On the Linear Speedup Analysis of Communication Efficient Momentum SGD for Distributed Non-Convex Optimization. (89 citations)
  • SoftTriple Loss: Deep Metric Learning Without Triplet Sampling (68 citations)
  • XNAS: Neural Architecture Search with Expert Advice (52 citations)

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

  • Artificial intelligence
  • Machine learning
  • Statistics

His primary areas of study are Artificial intelligence, Mathematical optimization, Artificial neural network, Deep learning and Speedup. His study looks at the relationship between Artificial intelligence and fields such as Machine learning, as well as how they intersect with chemical problems. His Mathematical optimization research includes elements of Rate of convergence, Convergence, Convex function and Robustness.

His biological study spans a wide range of topics, including Entropy estimation, Prior probability, Leverage and Image compression, Data compression ratio. In his research on the topic of Deep learning, Feature, Distributed computing and Sampling is strongly related with Benchmark. His research on Speedup also deals with topics like

  • Stochastic gradient descent, which have a strong connection to Big data and Parallel computing,
  • Communication complexity that intertwine with fields like Computation and Stochastic optimization.

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

Understanding bag-of-words model: A statistical framework

Yin Zhang;Rong Jin;Zhi Hua Zhou.
International Journal of Machine Learning and Cybernetics (2010)

502 Citations

Active Learning by Querying Informative and Representative Examples

Sheng-Jun Huang;Rong Jin;Zhi-Hua Zhou.
neural information processing systems (2010)

424 Citations

Batch mode active learning and its application to medical image classification

Steven C. H. Hoi;Rong Jin;Jianke Zhu;Michael R. Lyu.
international conference on machine learning (2006)

412 Citations

Flexible mixture model for collaborative filtering

Luo Si;Rong Jin.
international conference on machine learning (2003)

393 Citations

An automatic weighting scheme for collaborative filtering

Rong Jin;Joyce Y. Chai;Luo Si.
international acm sigir conference on research and development in information retrieval (2004)

373 Citations

SemiBoost: Boosting for Semi-Supervised Learning

P.K. Mallapragada;Rong Jin;A.K. Jain;Yi Liu.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2009)

367 Citations

Combining link and content for community detection: a discriminative approach

Tianbao Yang;Rong Jin;Yun Chi;Shenghuo Zhu.
knowledge discovery and data mining (2009)

349 Citations

Semisupervised SVM batch mode active learning with applications to image retrieval

Steven C. H. Hoi;Rong Jin;Jianke Zhu;Michael R. Lyu.
ACM Transactions on Information Systems (2009)

339 Citations

Discriminative Semi-Supervised Feature Selection Via Manifold Regularization

Zenglin Xu;Irwin King;Michael Rung-Tsong Lyu;Rong Jin.
IEEE Transactions on Neural Networks (2010)

309 Citations

Learning with Multiple Labels

Rong Jin;Zoubin Ghahramani.
neural information processing systems (2002)

299 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.

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Top Scientists Citing Rong Jin

Steven C. H. Hoi

Steven C. H. Hoi

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Irwin King

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Peilin Zhao

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Alexander G. Hauptmann

Alexander G. Hauptmann

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Tianbao Yang

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University of Iowa

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Meng Wang

Meng Wang

Hefei University of Technology

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Tao Mei

Tao Mei

Jingdong (China)

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Shuicheng Yan

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Feiping Nie

Feiping Nie

Northwestern Polytechnical University

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Huan Liu

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Qi Tian

Qi Tian

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