What is he best known for?
The fields of study Dahua Lin is best known for:
- Machine learning
- Artificial neural network
- Statistics
Dahua Lin combines topics linked to Convolutional neural network, Deep learning and Discriminative model with his work on Machine learning.
Dahua Lin incorporates Deep learning and Machine learning in his research.
In most of his Artificial intelligence studies, his work intersects topics such as Segmentation.
Segmentation is closely attributed to Artificial intelligence in his research.
Pattern recognition (psychology) and Facial recognition system are frequently intertwined in his study.
His work on Facial recognition system is being expanded to include thematically relevant topics such as Pattern recognition (psychology).
His research on Action recognition often connects related areas such as Class (philosophy).
Class (philosophy) and Action recognition are frequently intertwined in his study.
Dahua Lin performs multidisciplinary study in Quantum mechanics and Action (physics) in his work.
His most cited work include:
- Temporal Segment Networks: Towards Good Practices for Deep Action Recognition (1788 citations)
- Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition (1501 citations)
- PSANet: Point-wise Spatial Attention Network for Scene Parsing (524 citations)
What are the main themes of his work throughout his whole career to date
His Pattern recognition (psychology) research is multidisciplinary, incorporating perspectives in Convolutional neural network, Deep learning, Machine learning and Feature (linguistics), Linguistics.
He combines Machine learning and Artificial neural network in his research.
His Linguistics study often links to related topics such as Feature (linguistics).
He is involved in relevant fields of research such as Pattern recognition (psychology), Convolutional neural network, Image (mathematics), Artificial neural network, Deep learning, Segmentation and Class (philosophy) in the realm of Artificial intelligence.
He incorporates Quantum mechanics and Action (physics) in his studies.
Dahua Lin conducted interdisciplinary study in his works that combined Action (physics) and Quantum mechanics.
His Programming language study frequently involves adjacent topics like Set (abstract data type).
His Set (abstract data type) study frequently draws connections between related disciplines such as Programming language.
His Task (project management) study typically links adjacent topics like Management.
Dahua Lin most often published in these fields:
- Artificial intelligence (92.77%)
- Machine learning (51.81%)
- Pattern recognition (psychology) (45.78%)
What were the highlights of his more recent work (between 2020-2021)?
- Artificial intelligence (88.89%)
- Pattern recognition (psychology) (77.78%)
- Computer vision (55.56%)
In recent works Dahua Lin was focusing on the following fields of study:
In his study, Block (permutation group theory) is inextricably linked to Combinatorics, which falls within the broad field of Kernel (algebra).
In most of his Block (permutation group theory) studies, his work intersects topics such as Combinatorics.
Lidar combines with fields such as Point cloud and Remote sensing in his work.
In his works, he performs multidisciplinary study on Remote sensing and Lidar.
Many of his studies involve connections with topics such as Inpainting and Artificial intelligence.
Pattern recognition (psychology) connects with themes related to Object detection in his study.
His Object detection study frequently involves adjacent topics like Pattern recognition (psychology).
His Point cloud research extends to Computer vision, which is thematically connected.
In his work, he performs multidisciplinary research in Convolutional neural network and Deep learning.
Between 2020 and 2021, his most popular works were:
- Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulation (67 citations)
- Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulation (61 citations)
-
Distributions.jl: Definition and Modeling of Probability Distributions in the JuliaStats Ecosystem (18 citations)
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