H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 87 Citations 37,330 364 World Ranking 307 National Ranking 192

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Natural language processing
  • Programming language

Jianfeng Gao mainly focuses on Artificial intelligence, Natural language processing, Language model, Information retrieval and Artificial neural network. His Artificial intelligence study frequently links to related topics such as Machine learning. His work carried out in the field of Natural language processing brings together such families of science as Context, Recurrent neural network, Speech recognition, Word and Generative grammar.

He works mostly in the field of Language model, limiting it down to topics relating to Natural language understanding and, in certain cases, Deep neural networks, as a part of the same area of interest. His work deals with themes such as Ranking and Probabilistic latent semantic analysis, which intersect with Information retrieval. His study in Artificial neural network is interdisciplinary in nature, drawing from both Conversation and Bilinear interpolation.

His most cited work include:

  • Learning deep structured semantic models for web search using clickthrough data (1198 citations)
  • Stacked Attention Networks for Image Question Answering (1169 citations)
  • From captions to visual concepts and back (934 citations)

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

Jianfeng Gao mainly investigates Artificial intelligence, Natural language processing, Language model, Machine learning and Information retrieval. His study brings together the fields of Pattern recognition and Artificial intelligence. His Natural language processing study incorporates themes from Context, Speech recognition and Word.

He has researched Language model in several fields, including Trigram, Set, Transformer and Word error rate. Jianfeng Gao regularly links together related areas like Ranking in his Information retrieval studies. His Reinforcement learning study combines topics from a wide range of disciplines, such as Task completion and Human–computer interaction.

He most often published in these fields:

  • Artificial intelligence (70.63%)
  • Natural language processing (38.66%)
  • Language model (19.89%)

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

  • Artificial intelligence (70.63%)
  • Natural language processing (38.66%)
  • Language model (19.89%)

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

His main research concerns Artificial intelligence, Natural language processing, Language model, Transformer and Human–computer interaction. His work on Machine learning expands to the thematically related Artificial intelligence. His study in the field of Question answering is also linked to topics like Set.

His Language model research includes themes of Range, Embedding and Code. His Transformer research is multidisciplinary, incorporating elements of Object detection, Encoder, Inference, Residual and Transfer of learning. His Human–computer interaction study combines topics in areas such as Dialog box and Chatbot.

Between 2019 and 2021, his most popular works were:

  • On the Variance of the Adaptive Learning Rate and Beyond (160 citations)
  • Unified Vision-Language Pre-Training for Image Captioning and VQA (131 citations)
  • The Design and Implementation of XiaoIce, an Empathetic Social Chatbot (93 citations)

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

  • Artificial intelligence
  • Programming language
  • Machine learning

His primary areas of investigation include Artificial intelligence, Natural language processing, Language model, Transformer and Human–computer interaction. His Artificial intelligence study frequently links to adjacent areas such as Machine learning. The various areas that Jianfeng Gao examines in his Natural language processing study include Context, Generative grammar and Shot.

His studies in Language model integrate themes in fields like Information retrieval, Scale, Robustness and Product. His studies examine the connections between Transformer and genetics, as well as such issues in Closed captioning, with regards to Vocabulary and Unsupervised learning. His Human–computer interaction research integrates issues from Representation, Dialog system, Dialog box, Task analysis and Visualization.

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

Learning deep structured semantic models for web search using clickthrough data

Po-Sen Huang;Xiaodong He;Jianfeng Gao;Li Deng.
conference on information and knowledge management (2013)

1114 Citations

MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition

Yandong Guo;Lei Zhang;Yuxiao Hu;Xiaodong He.
european conference on computer vision (2016)

1034 Citations

Stacked Attention Networks for Image Question Answering

Zichao Yang;Xiaodong He;Jianfeng Gao;Li Deng.
computer vision and pattern recognition (2016)

950 Citations

A Diversity-Promoting Objective Function for Neural Conversation Models

Jiwei Li;Michel Galley;Chris Brockett;Jianfeng Gao.
north american chapter of the association for computational linguistics (2016)

919 Citations

From captions to visual concepts and back

Hao Fang;Saurabh Gupta;Forrest Iandola;Rupesh K. Srivastava.
computer vision and pattern recognition (2015)

900 Citations

Deep Reinforcement Learning for Dialogue Generation

Jiwei Li;Will Monroe;Alan Ritter;Dan Jurafsky.
empirical methods in natural language processing (2016)

754 Citations

A Neural Network Approach to Context-Sensitive Generation of Conversational Responses

Alessandro Sordoni;Michel Galley;Michael Auli;Chris Brockett.
north american chapter of the association for computational linguistics (2015)

691 Citations

Scalable training of L1-regularized log-linear models

Galen Andrew;Jianfeng Gao.
international conference on machine learning (2007)

637 Citations

Deep Reinforcement Learning for Dialogue Generation

Jiwei Li;Will Monroe;Alan Ritter;Michel Galley.
arXiv: Computation and Language (2016)

624 Citations

Embedding Entities and Relations for Learning and Inference in Knowledge Bases

Bishan Yang;Wen-tau Yih;Xiaodong He;Jianfeng Gao.
international conference on learning representations (2015)

620 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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