What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Operating system
His scientific interests lie mostly in Artificial intelligence, Machine learning, Pattern recognition, Theoretical computer science and Hash function.
His Deep learning, Classifier, Feature extraction, Kernel and Transfer of learning investigations are all subjects of Artificial intelligence research.
His Discriminative model study in the realm of Machine learning connects with subjects such as Transferability.
His Pattern recognition research is multidisciplinary, incorporating elements of Contextual image classification and Visual Word.
The various areas that Jianmin Wang examines in his Theoretical computer science study include Domain and Outlier.
In general Hash function study, his work on Hash table often relates to the realm of Binary code, thereby connecting several areas of interest.
His most cited work include:
- Learning Transferable Features with Deep Adaptation Networks (1056 citations)
- Learning Transferable Features with Deep Adaptation Networks (970 citations)
- Deep transfer learning with joint adaptation networks (801 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Data mining, Machine learning, Theoretical computer science and Process modeling.
His Artificial intelligence research includes themes of Domain, Domain adaptation and Pattern recognition.
Jianmin Wang studied Data mining and Business process that intersect with Petri net.
His work on Discriminative model as part of general Machine learning study is frequently linked to Transferability, therefore connecting diverse disciplines of science.
His research investigates the connection with Theoretical computer science and areas like Hash function which intersect with concerns in Image retrieval, Nearest neighbor search and Quantization.
His Process modeling research is multidisciplinary, incorporating perspectives in Business process management, Process mining, Business process modeling and Business process discovery.
He most often published in these fields:
- Artificial intelligence (38.75%)
- Data mining (24.66%)
- Machine learning (20.60%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (38.75%)
- Machine learning (20.60%)
- Domain (6.50%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Domain, Algorithm and Domain adaptation.
The concepts of his Artificial intelligence study are interwoven with issues in Task and Pattern recognition.
His research in the fields of Transfer of learning and Feature overlaps with other disciplines such as Structure, Transferability and Focus.
His Algorithm research also works with subjects such as
- Hash function which connect with Quantization,
- Data value together with Encoding.
His Domain adaptation research integrates issues from Adversarial system and Classifier.
His study looks at the intersection of Process modeling and topics like Process mining with Data mining.
Between 2017 and 2021, his most popular works were:
- Conditional Adversarial Domain Adaptation (518 citations)
- Multi-Adversarial Domain Adaptation. (188 citations)
- Partial Transfer Learning with Selective Adversarial Networks (166 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Operating system
Artificial intelligence, Machine learning, Domain, Domain adaptation and Deep learning are his primary areas of study.
His Artificial intelligence study incorporates themes from Theoretical computer science and Pattern recognition.
Jianmin Wang undertakes interdisciplinary study in the fields of Machine learning and Transferability through his research.
His studies deal with areas such as Adversarial system and Classifier as well as Domain adaptation.
His work deals with themes such as Embedding, Hash function and Categorization, which intersect with Deep learning.
As a part of the same scientific family, Jianmin Wang mostly works in the field of Adaptation, focusing on Stochastic gradient descent and, on occasion, State and Algorithm.
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