What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Algorithm
Artificial intelligence, Machine learning, Pattern recognition, Discriminative model and Adversarial system are his primary areas of study.
Chang Xu integrates Artificial intelligence and Generator in his studies.
As part of the same scientific family, he usually focuses on Machine learning, concentrating on Outlier and intersecting with Sparse approximation, Multi label learning, CURE data clustering algorithm and Canopy clustering algorithm.
His Pattern recognition study integrates concerns from other disciplines, such as Discrete mathematics, Pascal, Contextual image classification and Multiple view.
Chang Xu has included themes like Nonlinear dimensionality reduction, Feature and Clustering high-dimensional data in his Discriminative model study.
His studies in Adversarial system integrate themes in fields like Generative grammar and Transformer.
His most cited work include:
- A Survey on Multi-view Learning (626 citations)
- Multi-View Intact Space Learning (299 citations)
- Large-Margin Multi-ViewInformation Bottleneck (209 citations)
What are the main themes of his work throughout his whole career to date?
Chang Xu mainly investigates Artificial intelligence, Machine learning, Pattern recognition, Artificial neural network and Benchmark.
His study brings together the fields of Computer vision and Artificial intelligence.
He has researched Machine learning in several fields, including Training set and Robustness.
His Pattern recognition study combines topics in areas such as Contextual image classification, Subspace topology and Kernel.
His biological study spans a wide range of topics, including Data mining and Pruning.
His work carried out in the field of Benchmark brings together such families of science as Discrete cosine transform and Image translation.
He most often published in these fields:
- Artificial intelligence (64.65%)
- Machine learning (28.84%)
- Pattern recognition (21.86%)
What were the highlights of his more recent work (between 2020-2021)?
- Benchmark (18.60%)
- Artificial intelligence (64.65%)
- Algorithm (12.09%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Benchmark, Artificial intelligence, Algorithm, Machine learning and Convolutional neural network.
The various areas that Chang Xu examines in his Benchmark study include Frequency domain and Data mining.
His research integrates issues of Proxy and Dimension in his study of Artificial intelligence.
Chang Xu studied Algorithm and Artificial neural network that intersect with Regularization, Hyperparameter and Dropout.
His work in the fields of Machine learning, such as Support vector machine and Multi-label classification, overlaps with other areas such as Weighting, Current and Upgrade.
His Convolutional neural network study combines topics from a wide range of disciplines, such as Computer engineering, Mobile device, Knowledge engineering and Computational resource.
Between 2020 and 2021, his most popular works were:
- Learning Student Networks via Feature Embedding (18 citations)
- Locally Free Weight Sharing for Network Width Search (2 citations)
- Manifold Regularized Dynamic Network Pruning. (1 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
Chang Xu mainly focuses on Algorithm, Embedding, Feature, Base and Rank.
His research in Algorithm intersects with topics in Network architecture, Artificial neural network, Training set and Pruning.
To a larger extent, he studies Artificial intelligence with the aim of understanding Embedding.
His Feature research incorporates elements of Computational complexity theory, Computational resource and Mobile device.
He connects Base with Benchmark in his study.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
