What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Quantum mechanics
- Machine learning
Xi Peng mostly deals with Artificial intelligence, Pattern recognition, Subspace topology, Cluster analysis and Embedding.
His Artificial intelligence study incorporates themes from Generalization and Computer vision.
His studies in Pattern recognition integrate themes in fields like Facial recognition system and Network model.
His Subspace topology research is multidisciplinary, incorporating elements of Sparse approximation, Representation, Mathematical optimization and Data set.
The concepts of his Cluster analysis study are interwoven with issues in Graph and Data mining.
His work focuses on many connections between Embedding and other disciplines, such as Training set, that overlap with his field of interest in Gaussian noise.
His most cited work include:
- Accelerating magnetic resonance imaging via deep learning (376 citations)
- A Generative Adversarial Approach for Zero-Shot Learning from Noisy Texts (231 citations)
- Constructing the L2-Graph for Robust Subspace Learning and Subspace Clustering (149 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Pattern recognition, Computer vision, Machine learning and Image.
His Artificial neural network, Embedding, Subspace topology, Deep learning and Training set investigations are all subjects of Artificial intelligence research.
His studies deal with areas such as Representation and Cluster analysis as well as Subspace topology.
Xi Peng works mostly in the field of Pattern recognition, limiting it down to concerns involving Graph and, occasionally, Theoretical computer science.
He interconnects Facial expression and Compressed sensing in the investigation of issues within Computer vision.
His Machine learning study combines topics in areas such as Generalization and Pose.
He most often published in these fields:
- Artificial intelligence (65.37%)
- Pattern recognition (26.34%)
- Computer vision (22.44%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (65.37%)
- Machine learning (16.10%)
- Image (13.17%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Machine learning, Image, Artificial neural network and Cluster analysis.
His Artificial intelligence research includes themes of Modal, Computer vision and Pattern recognition.
His Machine learning research includes elements of Bayesian probability, Generalization, Training set and Joint.
As part of the same scientific family, he usually focuses on Image, concentrating on Representation and intersecting with Range, Trajectory and Noise.
As a part of the same scientific family, Xi Peng mostly works in the field of Artificial neural network, focusing on Data set and, on occasion, Iterative reconstruction.
His Cluster analysis research is multidisciplinary, incorporating perspectives in Data mining, Graph and Cluster.
Between 2019 and 2021, his most popular works were:
- Partition level multiview subspace clustering. (45 citations)
- Deep Clustering With Sample-Assignment Invariance Prior (39 citations)
- Learning to Learn Single Domain Generalization (29 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Quantum mechanics
- Machine learning
Xi Peng spends much of his time researching Artificial intelligence, Cluster analysis, Machine learning, Generalization and Deep learning.
His work blends Artificial intelligence and Task analysis studies together.
His research in Cluster analysis intersects with topics in Data point, Data mining and Graph.
His biological study spans a wide range of topics, including Anomaly detection and Pattern recognition.
His Pattern recognition research integrates issues from Recurrent neural network, Anomaly, Fuzzy clustering, Representation and Manifold.
His work in Feature extraction addresses issues such as Nonlinear dimensionality reduction, which are connected to fields such as Algorithm.
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