What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Algorithm
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Linear discriminant analysis, Cluster analysis and Dimensionality reduction.
His work deals with themes such as Machine learning, Graph and Data mining, which intersect with Artificial intelligence.
His Pattern recognition study combines topics in areas such as Subspace topology, Facial recognition system and Embedding.
In Linear discriminant analysis, Deng Cai works on issues like Relevance feedback, which are connected to Document retrieval.
In his study, Group is strongly linked to Information retrieval, which falls under the umbrella field of Cluster analysis.
His Dimensionality reduction study combines topics from a wide range of disciplines, such as Regression analysis and Optimization problem.
His most cited work include:
- Graph Regularized Nonnegative Matrix Factorization for Data Representation (1506 citations)
- Laplacian Score for Feature Selection (1251 citations)
- Neighborhood preserving embedding (1235 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Pattern recognition, Machine learning, Information retrieval and Data mining.
His work carried out in the field of Artificial intelligence brings together such families of science as Computer vision and Natural language processing.
Deng Cai usually deals with Pattern recognition and limits it to topics linked to Pixel and Segmentation.
He works mostly in the field of Machine learning, limiting it down to topics relating to Representation and, in certain cases, Discriminative model, as a part of the same area of interest.
His Information retrieval study integrates concerns from other disciplines, such as Graph, Ranking and Web page.
Deng Cai works mostly in the field of Dimensionality reduction, limiting it down to concerns involving Embedding and, occasionally, Nonlinear dimensionality reduction.
He most often published in these fields:
- Artificial intelligence (58.98%)
- Pattern recognition (34.92%)
- Machine learning (15.59%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (58.98%)
- Artificial neural network (6.78%)
- Object detection (5.08%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Artificial intelligence, Artificial neural network, Object detection, Computer vision and Pattern recognition.
His Artificial intelligence study frequently links to related topics such as Machine learning.
His Artificial neural network study combines topics from a wide range of disciplines, such as Adversarial system, Regularization, Algorithm, Relation and Mathematical optimization.
His studies deal with areas such as Similarity measure, Detector and Metric as well as Object detection.
His study explores the link between Computer vision and topics such as Benchmark that cross with problems in Machine translation, Sequence, Speech recognition and Encoding.
His work deals with themes such as Contextual image classification, Image and Feature, which intersect with Pattern recognition.
Between 2019 and 2021, his most popular works were:
- Adversarial-Learned Loss for Domain Adaptation (20 citations)
- Moment Retrieval via Cross-Modal Interaction Networks With Query Reconstruction (11 citations)
- Training-Time-Friendly Network for Real-Time Object Detection (9 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
Deng Cai mainly focuses on Artificial intelligence, Code, Machine learning, Representation and Information retrieval.
Artificial intelligence is closely attributed to Computer vision in his work.
His studies in Code integrate themes in fields like Algorithm and Simple.
Deng Cai combines topics linked to Classifier with his work on Machine learning.
He interconnects Speech recognition, Encoding and Sequence in the investigation of issues within Representation.
His study in the field of Automatic summarization also crosses realms of Context model, Moment and Interaction network.
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