What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Algorithm
Ya Zhang focuses on Artificial intelligence, Machine learning, Semi-supervised learning, Training set and Categorization.
His work on Leverage as part of general Artificial intelligence research is often related to Encoder, thus linking different fields of science.
His Semi-supervised learning study combines topics in areas such as Stability, Active learning and Unsupervised learning.
Much of his study explores Training set relationship to Information retrieval.
The concepts of his Categorization study are interwoven with issues in Object, Visualization and Inference.
His study in Ranking is interdisciplinary in nature, drawing from both Dynamic Bayesian network, Click path and Click-through rate.
His most cited work include:
- Expected reciprocal rank for graded relevance (630 citations)
- A dynamic bayesian network click model for web search ranking (428 citations)
- Actional-Structural Graph Convolutional Networks for Skeleton-Based Action Recognition (163 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Machine learning, Pattern recognition, Data mining and Algorithm are his primary areas of study.
His study in Support vector machine, Training set, Segmentation, Discriminative model and Leverage is carried out as part of his studies in Artificial intelligence.
His is involved in several facets of Machine learning study, as is seen by his studies on Active learning, Semi-supervised learning, Learning to rank, Recommender system and Ranking.
Ya Zhang regularly links together related areas like Stability in his Active learning studies.
His Recommender system research is included under the broader classification of Information retrieval.
His study looks at the relationship between Pattern recognition and fields such as Object, as well as how they intersect with chemical problems.
He most often published in these fields:
- Artificial intelligence (65.89%)
- Machine learning (33.64%)
- Pattern recognition (27.10%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (65.89%)
- Pattern recognition (27.10%)
- Machine learning (33.64%)
In recent papers he was focusing on the following fields of study:
Ya Zhang mainly focuses on Artificial intelligence, Pattern recognition, Machine learning, Theoretical computer science and Graph.
Artificial intelligence is a component of his Segmentation, Mutual information, Benchmark, Feature learning and Artificial neural network studies.
The study incorporates disciplines such as Vertex and Entropy in addition to Pattern recognition.
The various areas that Ya Zhang examines in his Machine learning study include Training set and Robustness.
His work carried out in the field of Theoretical computer science brings together such families of science as Embedding, Representation, Inference and Action.
He works mostly in the field of Graph, limiting it down to topics relating to Interpretability and, in certain cases, Human motion, as a part of the same area of interest.
Between 2019 and 2021, his most popular works were:
- Dynamic Multiscale Graph Neural Networks for 3D Skeleton Based Human Motion Prediction (30 citations)
- Bottom-Up Temporal Action Localization with Mutual Regularization. (13 citations)
- Iteratively-Refined Interactive 3D Medical Image Segmentation With Multi-Agent Reinforcement Learning (7 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
His primary areas of investigation include Artificial intelligence, Pattern recognition, Graph, Feature learning and Encoder.
His Artificial intelligence study frequently draws connections to adjacent fields such as Machine learning.
His study explores the link between Machine learning and topics such as Image segmentation that cross with problems in Reinforcement learning.
His study ties his expertise on Object together with the subject of Pattern recognition.
His work deals with themes such as Vertex, Graph classification, Mutual information and Pooling, which intersect with Feature learning.
His Graph neural networks research includes elements of Theoretical computer science, Action recognition, Action, Head and Skeleton.
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