D-Index & Metrics Best Publications

D-Index & Metrics

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 65 Citations 19,877 397 World Ranking 1117 National Ranking 104

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Gene
  • Machine learning

Xiaolong Wang focuses on Artificial intelligence, Pattern recognition, Machine learning, Feature vector and Support vector machine. His Artificial intelligence study frequently draws connections to other fields, such as Natural language processing. His work on Unsupervised learning is typically connected to Estimation as part of general Pattern recognition study, connecting several disciplines of science.

His Feature vector study combines topics from a wide range of disciplines, such as Genome, Sequence analysis, Theoretical computer science and Bioinformatics. The Feature extraction study combines topics in areas such as Object detection and Facial recognition system. His work carried out in the field of Object detection brings together such families of science as Segmentation, Process, Pose and Block.

His most cited work include:

  • Non-local Neural Networks (2476 citations)
  • Unsupervised Learning of Visual Representations Using Videos (641 citations)
  • Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences (520 citations)

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

His primary areas of study are Artificial intelligence, Pattern recognition, Machine learning, Natural language processing and Information retrieval. His study in Artificial neural network, Deep learning, Support vector machine, Feature extraction and Conditional random field are all subfields of Artificial intelligence. His Artificial neural network study frequently draws parallels with other fields, such as Convolutional neural network.

His Pattern recognition study integrates concerns from other disciplines, such as Data mining, Feature and Cluster analysis. His Natural language processing study combines topics from a wide range of disciplines, such as Principle of maximum entropy, Speech recognition and Named-entity recognition. His study on Information retrieval is mostly dedicated to connecting different topics, such as Web page.

He most often published in these fields:

  • Artificial intelligence (44.96%)
  • Pattern recognition (17.24%)
  • Machine learning (15.45%)

What were the highlights of his more recent work (between 2017-2021)?

  • Artificial intelligence (44.96%)
  • Gene (6.90%)
  • Machine learning (15.45%)

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

His primary areas of investigation include Artificial intelligence, Gene, Machine learning, Artificial neural network and Convolutional neural network. He has included themes like Pattern recognition, Computer vision and Natural language processing in his Artificial intelligence study. The subject of his Gene research is within the realm of Genetics.

Machine learning is closely attributed to Robustness in his work. He works in the field of Artificial neural network, namely Recurrent neural network. His Convolutional neural network research is multidisciplinary, incorporating elements of Sentence, Face and Benchmark.

Between 2017 and 2021, his most popular works were:

  • Non-local Neural Networks (2476 citations)
  • Videos as Space-Time Region Graphs (397 citations)
  • Zero-Shot Recognition via Semantic Embeddings and Knowledge Graphs (283 citations)

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

  • Artificial intelligence
  • Gene
  • Machine learning

His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Machine learning, Convolutional neural network and Gene. His work in Artificial intelligence covers topics such as Natural language processing which are related to areas like Task analysis. His studies in Machine learning integrate themes in fields like Contextual image classification, Representation, Robustness and Benchmark data.

His Convolutional neural network study incorporates themes from Sentence, Face and Benchmark. His biological study deals with issues like Hair follicle, which deal with fields such as DNA microarray, microRNA, Cashmere goat and Proteomics. His biological study spans a wide range of topics, including Segmentation, Image segmentation and Mobile device.

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

Non-local Neural Networks

Xiaolong Wang;Ross Girshick;Abhinav Gupta;Kaiming He.
computer vision and pattern recognition (2018)

1554 Citations

Molecular-Scale Electronics: From Concept to Function

Dong Xiang;Dong Xiang;Xiaolong Wang;Chuancheng Jia;Takhee Lee.
Chemical Reviews (2016)

741 Citations

Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences

Bin Liu;Fule Liu;Xiaolong Wang;Junjie Chen.
Nucleic Acids Research (2015)

631 Citations

Topic sentiment analysis in twitter: a graph-based hashtag sentiment classification approach

Xiaolong Wang;Furu Wei;Xiaohua Liu;Ming Zhou.
conference on information and knowledge management (2011)

496 Citations

Unsupervised Learning of Visual Representations Using Videos

Xiaolong Wang;Abhinav Gupta.
international conference on computer vision (2015)

485 Citations

Hollywood in Homes: Crowdsourcing Data Collection for Activity Understanding

Gunnar A. Sigurdsson;Gül Varol;Xiaolong Wang;Ali Farhadi;Ali Farhadi.
european conference on computer vision (2016)

420 Citations

Generative Image Modeling Using Style and Structure Adversarial Networks

Xiaolong Wang;Abhinav Gupta.
european conference on computer vision (2016)

409 Citations

Molecularly Engineered Dual‐Crosslinked Hydrogel with Ultrahigh Mechanical Strength, Toughness, and Good Self‐Recovery

Peng Lin;Shuanhong Ma;Xiaolong Wang;Feng Zhou.
Advanced Materials (2015)

404 Citations

Videos as Space-Time Region Graphs

Xiaolong Wang;Abhinav Gupta.
european conference on computer vision (2018)

397 Citations

repDNA: a Python package to generate various modes of feature vectors for DNA sequences by incorporating user-defined physicochemical properties and sequence-order effects.

Bin Liu;Fule Liu;Longyun Fang;Xiaolong Wang.
Bioinformatics (2015)

294 Citations

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Best Scientists Citing Xiaolong Wang

Feng Zhou

Feng Zhou

Lanzhou Institute of Chemical Physics

Publications: 80

Kuo-Chen Chou

Kuo-Chen Chou

The Gordon Life Science Institute

Publications: 79

Quan Zou

Quan Zou

University of Electronic Science and Technology of China

Publications: 72

Liang Lin

Liang Lin

Sun Yat-sen University

Publications: 63

Wei Chen

Wei Chen

North China University of Science and Technology

Publications: 53

Abhinav Gupta

Abhinav Gupta

Facebook (United States)

Publications: 51

Lei Jiang

Lei Jiang

Chinese Academy of Sciences

Publications: 50

Ming-Hsuan Yang

Ming-Hsuan Yang

University of California, Merced

Publications: 36

Andrew Zisserman

Andrew Zisserman

University of Oxford

Publications: 34

Xiaodan Liang

Xiaodan Liang

Sun Yat-sen University

Publications: 34

Bo Yu

Bo Yu

Chinese Academy of Sciences

Publications: 32

Alan L. Yuille

Alan L. Yuille

Johns Hopkins University

Publications: 32

Zijian Zheng

Zijian Zheng

Hong Kong Polytechnic University

Publications: 32

Antonio Torralba

Antonio Torralba

MIT

Publications: 31

Qi Tian

Qi Tian

Huawei Technologies (China)

Publications: 30

Alexei A. Efros

Alexei A. Efros

University of California, Berkeley

Publications: 30

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