His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Computer vision, Algorithm and Discriminative model. His work in Compressed sensing, Convolutional neural network, Cluster analysis, Feature selection and Multispectral image is related to Artificial intelligence. His Pattern recognition study integrates concerns from other disciplines, such as Histogram and Facial recognition system.
His Computer vision research is multidisciplinary, incorporating elements of Subspace topology, Mr imaging and Mr images. His study in the fields of Regularization under the domain of Algorithm overlaps with other disciplines such as Degradation, Speedup and Bottleneck. His work deals with themes such as Feature learning and Categorization, which intersect with Discriminative model.
Junzhou Huang mostly deals with Artificial intelligence, Pattern recognition, Computer vision, Machine learning and Theoretical computer science. His Artificial intelligence study focuses mostly on Deep learning, Segmentation, Image, Feature extraction and Image segmentation. His research in Pattern recognition intersects with topics in Feature and Compressed sensing.
Junzhou Huang interconnects Generalization and Representation in the investigation of issues within Machine learning. The concepts of his Theoretical computer science study are interwoven with issues in Feature learning and Graph. His study in Graph is interdisciplinary in nature, drawing from both Embedding and Graph.
Junzhou Huang mainly investigates Artificial intelligence, Machine learning, Theoretical computer science, Graph and Graph. His studies link Pattern recognition with Artificial intelligence. His Pattern recognition research is multidisciplinary, incorporating perspectives in Modality and Medical imaging.
His Machine learning research focuses on subjects like Annotation, which are linked to Liver segmentation. His Graph research is multidisciplinary, relying on both Artificial neural network, Recurrent neural network, Algorithm and Message passing. His studies deal with areas such as Adversarial system and Embedding as well as Graph.
Junzhou Huang focuses on Artificial intelligence, Graph, Theoretical computer science, Machine learning and Graph. His Artificial intelligence research includes themes of Sample, Adaptation and Pattern recognition. Segmentation is closely connected to Translation in his research, which is encompassed under the umbrella topic of Pattern recognition.
The study incorporates disciplines such as Autoencoder, Clustering coefficient, Taylor series and Computation in addition to Theoretical computer science. Junzhou Huang combines subjects such as Cover and Face with his study of Machine learning. His Graph embedding study in the realm of Graph interacts with subjects such as Rumor.
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.
Robust tracking using local sparse appearance model and K-selection
Baiyang Liu;Junzhou Huang;Lin Yang;Casimir Kulikowsk.
computer vision and pattern recognition (2011)
Learning with Structured Sparsity
Junzhou Huang;Tong Zhang;Dimitris Metaxas.
Journal of Machine Learning Research (2011)
The Benefit of Group Sparsity
Junzhou Huang;Tong Zhang.
Annals of Statistics (2010)
Learning active facial patches for expression analysis
Lin Zhong;Qingshan Liu;Peng Yang;Bo Liu.
computer vision and pattern recognition (2012)
Efficient MR image reconstruction for compressed MR imaging
Junzhou Huang;Shaoting Zhang;Dimitris N. Metaxas.
Medical Image Analysis (2011)
Large-scale multi-view spectral clustering via bipartite graph
Yeqing Li;Feiping Nie;Heng Huang;Junzhou Huang.
national conference on artificial intelligence (2015)
Pose-Free Facial Landmark Fitting via Optimized Part Mixtures and Cascaded Deformable Shape Model
Xiang Yu;Junzhou Huang;Shaoting Zhang;Wang Yan.
international conference on computer vision (2013)
Robust and fast collaborative tracking with two stage sparse optimization
Baiyang Liu;Lin Yang;Junzhou Huang;Peter Meer.
european conference on computer vision (2010)
Adaptive Graph Convolutional Neural Networks
Ruoyu Li;Sheng Wang;Feiyun Zhu;Junzhou Huang.
national conference on artificial intelligence (2018)
Discrimination-aware channel pruning for deep neural networks
Zhuangwei Zhuang;Mingkui Tan;Bohan Zhuang;Jing Liu.
neural information processing systems (2018)
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