What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Rui Yan mainly investigates Artificial intelligence, Natural language processing, Conversation, Generative grammar and Artificial neural network.
His Artificial intelligence research includes themes of Machine learning and Poetry.
His work on Sentence and Automatic summarization as part of general Natural language processing research is frequently linked to Metric, thereby connecting diverse disciplines of science.
The study incorporates disciplines such as Entropy, Entropy, Noun and Pointwise mutual information in addition to Conversation.
His Generative grammar study incorporates themes from Entropy, Context and Dialog box.
In the field of Artificial neural network, his study on Overfitting overlaps with subjects such as Image processing, Transferability and Entropy.
His most cited work include:
- Style Transfer in Text: Exploration and Evaluation. (229 citations)
- Natural Language Inference by Tree-Based Convolution and Heuristic Matching (226 citations)
- Learning to Respond with Deep Neural Networks for Retrieval-Based Human-Computer Conversation System (189 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Natural language processing, Information retrieval, Plasma and Atomic physics.
Rui Yan has researched Artificial intelligence in several fields, including Machine learning and Conversation.
His research investigates the connection with Conversation and areas like Context which intersect with concerns in Human–computer interaction.
Rui Yan combines subjects such as Word, Autoencoder, Generative grammar and Dialog box with his study of Natural language processing.
His Information retrieval study incorporates themes from Matching and E-commerce.
In his study, which falls under the umbrella issue of Plasma, Hot electron is strongly linked to Laser.
He most often published in these fields:
- Artificial intelligence (43.70%)
- Natural language processing (25.56%)
- Information retrieval (15.56%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (43.70%)
- Matching (10.37%)
- Information retrieval (15.56%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Matching, Information retrieval, Selection and Context.
His Artificial intelligence research includes elements of Machine learning and Natural language processing.
When carried out as part of a general Natural language processing research project, his work on WordNet is frequently linked to work in Sequence, therefore connecting diverse disciplines of study.
His work in the fields of Automatic summarization and Relevance overlaps with other areas such as Generator and Process.
His research in Selection intersects with topics in Task, Conversation, Human–computer interaction and Benchmark.
His Context research incorporates themes from Artificial neural network, Session, Dialog box and Feature.
Between 2019 and 2021, his most popular works were:
- Low-Resource Knowledge-Grounded Dialogue Generation (20 citations)
- Learning to Respond with Stickers: A Framework of Unifying Multi-Modality in Multi-Turn Dialog (11 citations)
- A Character-Centric Neural Model for Automated Story Generation (11 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Rui Yan spends much of his time researching Information retrieval, Selection, Matching, Human–computer interaction and Artificial intelligence.
He works mostly in the field of Information retrieval, limiting it down to topics relating to Dialog box and, in certain cases, Utterance, as a part of the same area of interest.
His Selection study also includes
- Context together with Artificial neural network,
- Benchmark which intersects with area such as Conversation, Semantic matching and Adversarial system.
The various areas that Rui Yan examines in his Artificial neural network study include Semantics and Representation.
His Human–computer interaction research is multidisciplinary, incorporating perspectives in Reinforcement learning, Knowledge base and Forcing.
His Machine learning research extends to the thematically linked field of Artificial intelligence.
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