What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Geometry
- Mathematical analysis
Topology, Algorithm, Polygon mesh, Computation and Computer graphics are his primary areas of study.
His Topology study combines topics from a wide range of disciplines, such as Hexahedron, Surface, Mesh parameterization, Variational method and Image-based meshing.
His Algorithm study combines topics in areas such as Point cloud, Ellipse, Feature, Rotation and Voxel.
His study focuses on the intersection of Polygon mesh and fields such as Geometric modeling with connections in the field of Conical surface, Volume mesh, Developable surface, Principal curvature and Computational geometry.
The various areas that he examines in his Computation study include Voronoi diagram, Centroidal Voronoi tessellation, Mean curvature and Mathematical optimization.
The study incorporates disciplines such as Function and Visualization, Data visualization in addition to Computer graphics.
His most cited work include:
- O-CNN: octree-based convolutional neural networks for 3D shape analysis (503 citations)
- Geometric modeling with conical meshes and developable surfaces (307 citations)
- Fitting B-spline curves to point clouds by curvature-based squared distance minimization (265 citations)
What are the main themes of his work throughout his whole career to date?
Yang Liu spends much of his time researching Artificial intelligence, Algorithm, Polygon mesh, Topology and Surface.
His Artificial intelligence study integrates concerns from other disciplines, such as Natural language processing, Computer vision and Pattern recognition.
His studies examine the connections between Algorithm and genetics, as well as such issues in Mathematical optimization, with regards to Curve fitting, Applied mathematics and Computer graphics.
His study in Polygon mesh is interdisciplinary in nature, drawing from both Parametrization, Hexahedron and Volume mesh.
As a part of the same scientific family, Yang Liu mostly works in the field of Topology, focusing on Centroidal Voronoi tessellation and, on occasion, Generalization.
As part of the same scientific family, Yang Liu usually focuses on Surface, concentrating on Voronoi diagram and intersecting with Computation and Speedup.
He most often published in these fields:
- Artificial intelligence (24.56%)
- Algorithm (19.30%)
- Polygon mesh (19.30%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (24.56%)
- Octree (8.77%)
- Convolutional neural network (7.89%)
In recent papers he was focusing on the following fields of study:
Yang Liu focuses on Artificial intelligence, Octree, Convolutional neural network, Algorithm and Data mining.
Much of his study explores Artificial intelligence relationship to Pattern recognition.
His Octree research is within the category of Computer vision.
His work carried out in the field of Algorithm brings together such families of science as Parametrization, Polygon mesh and Surface.
His Polygon mesh research is multidisciplinary, incorporating elements of Feature and Global optimization.
Yang Liu has included themes like Artificial neural network and Point in his Surface study.
Between 2018 and 2021, his most popular works were:
- Learning adaptive hierarchical cuboid abstractions of 3D shape collections (22 citations)
- OoDAnalyzer: Interactive Analysis of Out-of-Distribution Samples. (8 citations)
- Overview of the Ninth Dialog System Technology Challenge: DSTC9 (7 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Geometry
- Mathematical analysis
Yang Liu mostly deals with Dialog box, Data mining, Polygon mesh, Artificial intelligence and Convolutional neural network.
His study in the fields of Dialog system under the domain of Dialog box overlaps with other disciplines such as Open domain and Task.
His Visualization study in the realm of Data mining interacts with subjects such as Scale and Key.
His work deals with themes such as Algorithm, Convolution, Surface and Kernel, which intersect with Polygon mesh.
His work on Shape analysis, Unsupervised learning and Representation as part of his general Artificial intelligence study is frequently connected to Class, thereby bridging the divide between different branches of science.
Yang Liu has researched Convolutional neural network in several fields, including Point cloud, Segmentation and Tangent.
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