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.
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