What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Artificial neural network
Fuzhen Zhuang mainly focuses on Artificial intelligence, Machine learning, Transfer of learning, Data mining and Extreme learning machine.
Fuzhen Zhuang performs integrative Artificial intelligence and Generalization research in his work.
His Feature study in the realm of Machine learning interacts with subjects such as Multi-task learning.
His Transfer of learning study combines topics from a wide range of disciplines, such as Domain, Data modeling, Data science and Semi-supervised learning.
His research integrates issues of Recurrent neural network, Classifier, Regularization, Control and Task in his study of Data mining.
His study looks at the relationship between Extreme learning machine and topics such as Kernel, which overlap with Kernelization, Kernel, Random projection, Continuous function and k-means clustering.
His most cited work include:
- Supervised representation learning: transfer learning with deep autoencoders (172 citations)
- Parallel extreme learning machine for regression based on MapReduce (127 citations)
- A Comprehensive Survey on Transfer Learning (119 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Artificial intelligence, Machine learning, Data mining, Recommender system and Pattern recognition.
His studies in Artificial intelligence integrate themes in fields like Domain and Natural language processing.
Fuzhen Zhuang is studying Feature learning, which is a component of Machine learning.
Fuzhen Zhuang interconnects Autoencoder and Softmax function in the investigation of issues within Feature learning.
His studies deal with areas such as Topic model, Regularization, Statistical classification, Speedup and Data set as well as Data mining.
His work in Recommender system tackles topics such as Embedding which are related to areas like Feature vector.
He most often published in these fields:
- Artificial intelligence (58.79%)
- Machine learning (44.22%)
- Data mining (21.61%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (58.79%)
- Machine learning (44.22%)
- Recommender system (20.60%)
In recent papers he was focusing on the following fields of study:
Fuzhen Zhuang spends much of his time researching Artificial intelligence, Machine learning, Recommender system, Embedding and Domain.
Artificial intelligence is often connected to Pattern recognition in his work.
His Pattern recognition research incorporates elements of Contextual image classification and Backpropagation.
His work on Recurrent neural network, Margin and Feature as part of general Machine learning research is often related to Term, thus linking different fields of science.
When carried out as part of a general Recommender system research project, his work on Cold start is frequently linked to work in Line, therefore connecting diverse disciplines of study.
The concepts of his Embedding study are interwoven with issues in Recommendation model, Theoretical computer science, Inference, Feature vector and Task.
Between 2019 and 2021, his most popular works were:
- A Comprehensive Survey on Transfer Learning (119 citations)
- A survey on knowledge graph-based recommender systems (21 citations)
- Deep Subdomain Adaptation Network for Image Classification (16 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Artificial neural network
Artificial intelligence, Machine learning, Feature extraction, Recommender system and Domain are his primary areas of study.
His work on Artificial intelligence deals in particular with Network model and Leverage.
His Network model research includes elements of Contextual image classification, Backpropagation, Kernel and Pattern recognition.
His Machine learning study typically links adjacent topics like Representation.
Fuzhen Zhuang has included themes like User experience design and Information explosion in his Recommender system study.
His research in Graph embedding tackles topics such as Data science which are related to areas like Transfer of learning.
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