What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Algorithm
His primary areas of investigation include Artificial intelligence, Pattern recognition, Computer vision, Shape analysis and Theoretical computer science.
Artificial intelligence and Algorithm are frequently intertwined in his study.
His Pattern recognition study combines topics in areas such as Function and Matching.
His Computer vision research is multidisciplinary, incorporating elements of Tree structure, Surface and Outlier.
His Shape analysis research incorporates themes from Machine learning, Active shape model and Data science.
His study looks at the relationship between Theoretical computer science and fields such as Geometry processing, as well as how they intersect with chemical problems.
His most cited work include:
- DualGAN: Unsupervised Dual Learning for Image-to-Image Translation (866 citations)
- A Survey on Shape Correspondence (473 citations)
- Learning Implicit Fields for Generative Shape Modeling (311 citations)
What are the main themes of his work throughout his whole career to date?
Hao Zhang spends much of his time researching Artificial intelligence, Pattern recognition, Computer vision, Algorithm and Segmentation.
His work in Artificial intelligence is not limited to one particular discipline; it also encompasses Machine learning.
In his work, Recurrent neural network is strongly intertwined with Autoencoder, which is a subfield of Pattern recognition.
Shape analysis and Point cloud are the primary areas of interest in his Computer vision study.
His work focuses on many connections between Algorithm and other disciplines, such as Polygon mesh, that overlap with his field of interest in Mathematical optimization and Theoretical computer science.
His Segmentation research includes elements of Structure and Convolutional neural network.
He most often published in these fields:
- Artificial intelligence (57.40%)
- Pattern recognition (27.08%)
- Computer vision (17.69%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (57.40%)
- Pattern recognition (27.08%)
- Artificial neural network (11.91%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Pattern recognition, Artificial neural network, Deep learning and Segmentation.
Hao Zhang interconnects Machine learning and Computer vision in the investigation of issues within Artificial intelligence.
His research integrates issues of 3D reconstruction, Feature, Metric and Net in his study of Pattern recognition.
The Recurrent neural network research Hao Zhang does as part of his general Artificial neural network study is frequently linked to other disciplines of science, such as Encoder, Privacy protection and Key, therefore creating a link between diverse domains of science.
His Deep learning research integrates issues from Game theoretic, Algorithm, Shape analysis and Distributed computing.
His work on Image segmentation as part of general Segmentation research is frequently linked to Set, bridging the gap between disciplines.
Between 2018 and 2021, his most popular works were:
- Learning Implicit Fields for Generative Shape Modeling (311 citations)
- SDM-NET: deep generative network for structured deformable mesh (84 citations)
- GRAINS: Generative Recursive Autoencoders for INdoor Scenes (71 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
Hao Zhang mostly deals with Artificial intelligence, Pattern recognition, Artificial neural network, Autoencoder and Generative grammar.
He combines subjects such as Sequence and Polygon mesh with his study of Artificial intelligence.
His work in Polygon mesh addresses subjects such as Computer vision, which are connected to disciplines such as Homeomorphism and Structural level.
His Pattern recognition study combines topics from a wide range of disciplines, such as Net, 3D reconstruction, Feature and Interpolation.
His study looks at the relationship between Autoencoder and topics such as Representation, which overlap with Unsupervised learning and Translation.
His studies in Generative grammar integrate themes in fields like Generator, Variety, Cognitive science and Dimension.
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