What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Operating system
- The Internet
Qing Li mainly investigates Information retrieval, Artificial intelligence, Data mining, World Wide Web and Database.
Qing Li has included themes like Linear programming, Pattern recognition and Natural language processing in his Artificial intelligence study.
The concepts of his Linear programming study are interwoven with issues in Contextual image classification, Entropy, Image quality and Image translation.
The Data mining study combines topics in areas such as Machine learning, Set and Trajectory.
His World Wide Web study combines topics in areas such as Multimedia, Cluster analysis and Distance education.
His work deals with themes such as Software engineering and k-nearest neighbors algorithm, which intersect with Database.
His most cited work include:
- Least Squares Generative Adversarial Networks (1888 citations)
- Unified Modeling Language (574 citations)
- Algorithms for Materialized View Design in Data Warehousing Environment (300 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Artificial intelligence, Data mining, Information retrieval, World Wide Web and Database.
His Artificial intelligence research is multidisciplinary, incorporating elements of Natural language processing, Task, Computer vision, Machine learning and Pattern recognition.
His Data mining research incorporates themes from Object, Set and Cluster analysis.
He works on Information retrieval which deals in particular with Search engine indexing.
His research in World Wide Web tackles topics such as Multimedia which are related to areas like The Internet.
Many of his studies involve connections with topics such as Software engineering and Database.
He most often published in these fields:
- Artificial intelligence (23.61%)
- Data mining (16.48%)
- Information retrieval (16.32%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (23.61%)
- Natural language processing (7.13%)
- Machine learning (6.66%)
In recent papers he was focusing on the following fields of study:
Qing Li spends much of his time researching Artificial intelligence, Natural language processing, Machine learning, Representation and Pattern recognition.
His Deep learning, Artificial neural network, Convolutional neural network, Embedding and Sentence study are his primary interests in Artificial intelligence.
His Natural language processing research incorporates elements of Word and Task.
His biological study spans a wide range of topics, including Domain, Social media, Feature learning and Cluster analysis.
Qing Li interconnects Adversarial system and Recommender system in the investigation of issues within Domain.
His Pattern recognition research includes themes of Image and Modal.
Between 2018 and 2021, his most popular works were:
- Graph Neural Networks for Social Recommendation (182 citations)
- On the Effectiveness of Least Squares Generative Adversarial Networks (59 citations)
- Multiobjective Cloud Workflow Scheduling: A Multiple Populations Ant Colony System Approach (48 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Operating system
- The Internet
His main research concerns Artificial intelligence, Natural language processing, Representation, Feature learning and Task.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning and Sequence.
The various areas that Qing Li examines in his Feature learning study include Adversarial system, Recommender system, Human–computer interaction and Gradient descent.
His research integrates issues of Embedding and Set in his study of Task.
As part of the same scientific family, Qing Li usually focuses on Convolutional neural network, concentrating on Hidden Markov model and intersecting with Data mining.
The study incorporates disciplines such as Benchmark and Pattern recognition in addition to Contextual image classification.
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