What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Algorithm
Heng Tao Shen focuses on Artificial intelligence, Hash function, Theoretical computer science, Data mining and Pattern recognition.
The various areas that he examines in his Artificial intelligence study include Machine learning and Graph.
His biological study spans a wide range of topics, including Binary code, Search engine indexing and Image retrieval.
His Data mining research integrates issues from Object, Motion, Algorithm design and Motion estimation.
His study in the field of Discriminative model is also linked to topics like Kernel principal component analysis.
His studies deal with areas such as Universal hashing and Dynamic perfect hashing as well as Feature hashing.
His most cited work include:
- Supervised Discrete Hashing (630 citations)
- A Survey on Learning to Hash (485 citations)
- l 2,1 -norm regularized discriminative feature selection for unsupervised learning (468 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Pattern recognition, Data mining, Machine learning and Hash function.
His Artificial intelligence study frequently involves adjacent topics like Computer vision.
His work deals with themes such as Contextual image classification, Embedding and Cluster analysis, which intersect with Pattern recognition.
As part of one scientific family, he deals mainly with the area of Data mining, narrowing it down to issues related to the Search engine indexing, and often Video tracking.
In the field of Machine learning, his study on Feature overlaps with subjects such as Modal.
Heng Tao Shen has included themes like Binary code and Theoretical computer science in his Hash function study.
He most often published in these fields:
- Artificial intelligence (49.88%)
- Pattern recognition (25.41%)
- Data mining (17.65%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (49.88%)
- Pattern recognition (25.41%)
- Machine learning (15.76%)
In recent papers he was focusing on the following fields of study:
Heng Tao Shen mainly investigates Artificial intelligence, Pattern recognition, Machine learning, Discriminative model and Embedding.
Heng Tao Shen performs integrative Artificial intelligence and Focus research in his work.
His Pattern recognition research includes elements of Contextual image classification and Benchmark.
His work on Feature as part of general Machine learning research is frequently linked to Modal, Knowledge transfer and Scheme, thereby connecting diverse disciplines of science.
His Discriminative model research incorporates elements of Iterative method, Hash function and Binary code.
His work carried out in the field of Feature extraction brings together such families of science as Semantics and Natural language.
Between 2018 and 2021, his most popular works were:
- Transfer Independently Together: A Generalized Framework for Domain Adaptation (133 citations)
- From Deterministic to Generative: Multimodal Stochastic RNNs for Video Captioning (125 citations)
- Binary Multi-View Clustering (121 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Theoretical computer science, Binary code and Feature extraction.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning and Natural language processing.
Heng Tao Shen combines subjects such as Contextual image classification, Similarity, Facial expression recognition and Feature with his study of Pattern recognition.
His studies in Theoretical computer science integrate themes in fields like Hash function, Divergence and Graph.
The Hash function study combines topics in areas such as Hamming space, Multimedia search and Image retrieval.
Heng Tao Shen interconnects Text mining and Semantics in the investigation of issues within Feature extraction.
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