What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- The Internet
Artificial intelligence, Information retrieval, Machine learning, Pattern recognition and Image retrieval are his primary areas of study.
His work deals with themes such as Recommender system and Data mining, which intersect with Artificial intelligence.
His Information retrieval study incorporates themes from Ranking, Context and World Wide Web, The Internet.
He interconnects Space and Key in the investigation of issues within Machine learning.
His studies deal with areas such as Speech recognition, Similarity and Kernel as well as Pattern recognition.
His work carried out in the field of Image retrieval brings together such families of science as Image processing and Annotation.
His most cited work include:
- NUS-WIDE: a real-world web image database from National University of Singapore (1753 citations)
- Neural Collaborative Filtering (1578 citations)
- SCA-CNN: Spatial and Channel-Wise Attention in Convolutional Networks for Image Captioning (731 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Information retrieval, Machine learning, Multimedia and World Wide Web.
The Artificial intelligence study combines topics in areas such as Natural language processing, Computer vision and Pattern recognition.
His research on Pattern recognition often connects related topics like Automatic image annotation.
He combines subjects such as Ranking, Semantics and Image retrieval with his study of Information retrieval.
His study brings together the fields of Data mining and Machine learning.
As part of his studies on World Wide Web, Tat-Seng Chua often connects relevant subjects like Data science.
He most often published in these fields:
- Artificial intelligence (43.20%)
- Information retrieval (26.04%)
- Machine learning (17.60%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (43.20%)
- Information retrieval (26.04%)
- Machine learning (17.60%)
In recent papers he was focusing on the following fields of study:
Tat-Seng Chua spends much of his time researching Artificial intelligence, Information retrieval, Machine learning, Recommender system and Embedding.
His research in Artificial intelligence intersects with topics in Relation and Natural language processing.
His Information retrieval research incorporates elements of Cover, Conversation and Encoding.
His Machine learning research incorporates themes from Domain knowledge and Bipartite graph.
His Recommender system study combines topics from a wide range of disciplines, such as Visualization, Noise reduction, Key and Empirical research.
His Embedding study combines topics in areas such as Ranking, Feature extraction and Social network.
Between 2019 and 2021, his most popular works were:
- Neural Sparse Voxel Fields (50 citations)
- Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems (46 citations)
- Adversarial Training Towards Robust Multimedia Recommender System (40 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- The Internet
His main research concerns Artificial intelligence, Information retrieval, Recommender system, Machine learning and Natural language.
His study in Deep learning, Representation, Leverage, Artificial neural network and Computer graphics is carried out as part of his Artificial intelligence studies.
His work deals with themes such as Ranking, Conversation and Empirical research, which intersect with Information retrieval.
His Recommender system research is multidisciplinary, incorporating perspectives in Visualization, Key and Converse.
The concepts of his Machine learning study are interwoven with issues in Relational graph and Knowledge graph.
His study in Natural language is interdisciplinary in nature, drawing from both World Wide Web, Information seeking, Data science and Word embedding.
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