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 33 Citations 4,518 87 World Ranking 6786 National Ranking 3231

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Statistics
  • Machine learning

His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Algorithm, Deep learning and Convolutional neural network. His Artificial intelligence research incorporates themes from Matching, Machine learning and Computer vision. His studies deal with areas such as Cognitive neuroscience of visual object recognition and 3D single-object recognition as well as Pattern recognition.

His work is dedicated to discovering how Algorithm, Intrinsic dimension are connected with Artificial neural network and other disciplines. The concepts of his Deep learning study are interwoven with issues in Bayesian probability, Support vector machine and Code. His study looks at the relationship between Convolutional neural network and fields such as Encoder, as well as how they intersect with chemical problems.

His most cited work include:

  • Variational autoencoder for deep learning of images, labels and captions (220 citations)
  • ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching (159 citations)
  • Preconditioned Stochastic Gradient Langevin Dynamics for deep neural networks (139 citations)

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

His main research concerns Artificial intelligence, Machine learning, Pattern recognition, Algorithm and Artificial neural network. His Artificial intelligence research incorporates elements of Computer vision and Natural language processing. His Machine learning study combines topics in areas such as Adversarial system, Body shape, Text generation and Language model.

His work deals with themes such as Contextual image classification and Generative model, which intersect with Pattern recognition. His research in Algorithm tackles topics such as Inference which are related to areas like Unsupervised learning. His Deep learning research includes themes of Convolutional neural network and Bayesian probability.

He most often published in these fields:

  • Artificial intelligence (62.41%)
  • Machine learning (27.82%)
  • Pattern recognition (18.80%)

What were the highlights of his more recent work (between 2019-2021)?

  • Artificial intelligence (62.41%)
  • Machine learning (27.82%)
  • Language model (14.29%)

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

Chunyuan Li focuses on Artificial intelligence, Machine learning, Language model, Natural language processing and Natural language. His research in Benchmark, Image, Text generation, Bayesian inference and Artificial neural network are components of Artificial intelligence. His research integrates issues of Recommender system, Collaborative filtering, Deep learning, Bayesian probability and Reinforcement learning in his study of Artificial neural network.

In his study, Robustness, Computer graphics and Body shape is strongly linked to Training set, which falls under the umbrella field of Machine learning. His Language model study incorporates themes from Word, Generative grammar, Transformer and Forcing. His Natural language processing study combines topics from a wide range of disciplines, such as Embedding and Autoencoder.

Between 2019 and 2021, his most popular works were:

  • SOLOIST: Few-shot Task-Oriented Dialog with A Single Pre-trained Auto-regressive Model (37 citations)
  • Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks (36 citations)
  • Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning (36 citations)

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

  • Artificial intelligence
  • Statistics
  • Machine learning

Chunyuan Li mainly investigates Artificial intelligence, Natural language processing, Language model, Natural language and Generative grammar. Artificial intelligence is often connected to Pattern recognition in his work. His biological study deals with issues like Object, which deal with fields such as Code.

His work carried out in the field of Language model brings together such families of science as Generative model and Transformer. The various areas that Chunyuan Li examines in his Natural language study include Embedding, Text corpus, Autoencoder and Feature learning. The study incorporates disciplines such as Theoretical computer science and Inference in addition to Generative grammar.

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

Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms

Dinghan Shen;Guoyin Wang;Wenlin Wang;Martin Renqiang Min.
meeting of the association for computational linguistics (2018)

262 Citations

Joint Embedding of Words and Labels for Text Classification

Guoyin Wang;Chunyuan Li;Wenlin Wang;Yizhe Zhang.
meeting of the association for computational linguistics (2018)

215 Citations

Preconditioned Stochastic Gradient Langevin Dynamics for deep neural networks

Chunyuan Li;Changyou Chen;David Carlson;Lawrence Carin.
national conference on artificial intelligence (2016)

205 Citations

Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks

Xiujun Li;Xi Yin;Chunyuan Li;Pengchuan Zhang.
european conference on computer vision (2020)

169 Citations

Variational autoencoder for deep learning of images, labels and captions

Yunchen Pu;Zhe Gan;Ricardo Henao;Xin Yuan.
neural information processing systems (2016)

167 Citations

ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching

Chunyuan Li;Hao Liu;Changyou Chen;Yunchen Pu.
neural information processing systems (2017)

164 Citations

A comparison of 3D shape retrieval methods based on a large-scale benchmark supporting multimodal queries

Bo Li;Yijuan Lu;Chunyuan Li;Afzal Godil.
Computer Vision and Image Understanding (2015)

127 Citations

Shape Retrieval of Non-rigid 3D Human Models

D. Pickup;X. Sun;P. L. Rosin;R. R. Martin.
International Journal of Computer Vision (2016)

124 Citations

A multiresolution descriptor for deformable 3D shape retrieval

Chunyuan Li;A. Ben Hamza.
The Visual Computer (2013)

117 Citations

Persistence-Based Structural Recognition

Chunyuan Li;Maks Ovsjanikov;Frederic Chazal.
computer vision and pattern recognition (2014)

106 Citations

Best Scientists Citing Chunyuan Li

Lawrence Carin

Lawrence Carin

King Abdullah University of Science and Technology

Publications: 76

Zhe Gan

Zhe Gan

Microsoft (United States)

Publications: 39

Jianfeng Gao

Jianfeng Gao

Microsoft (United States)

Publications: 21

Liqun Chen

Liqun Chen

University of Surrey

Publications: 17

Jun Zhu

Jun Zhu

Tsinghua University

Publications: 16

Minlie Huang

Minlie Huang

Tsinghua University

Publications: 12

Ying Nian Wu

Ying Nian Wu

University of California, Los Angeles

Publications: 11

Jingjing Liu

Jingjing Liu

Tsinghua University

Publications: 11

Yu Cheng

Yu Cheng

Microsoft (United States)

Publications: 10

Zicheng Liu

Zicheng Liu

Huazhong University of Science and Technology

Publications: 10

Pascale Fung

Pascale Fung

Hong Kong University of Science and Technology

Publications: 10

Bo Chen

Bo Chen

Xidian University

Publications: 10

Kevin Gimpel

Kevin Gimpel

Toyota Technological Institute at Chicago

Publications: 10

Jin Xie

Jin Xie

Nanjing University of Science and Technology

Publications: 10

Junsong Yuan

Junsong Yuan

University at Buffalo, State University of New York

Publications: 9

Jingren Zhou

Jingren Zhou

Alibaba Group (China)

Publications: 9

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking d-index is inferred from publications deemed to belong to the considered discipline.

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