The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Machine learning, Contextual image classification and Data mining. His Artificial intelligence study frequently draws connections between related disciplines such as Computer vision. His study in Machine learning is interdisciplinary in nature, drawing from both Structure, Probabilistic logic and Empirical research.
His studies in Contextual image classification integrate themes in fields like Vector quantization, Deep learning, Coding and Kernel. In his work, Image segmentation and Histogram is strongly intertwined with Caltech 101, which is a subfield of Neural coding. His Feature extraction research incorporates themes from Artificial neural network and Convolutional neural network.
His primary areas of study are Artificial intelligence, Machine learning, Pattern recognition, Internal medicine and Data mining. His research ties Computer vision and Artificial intelligence together. In the subject of general Machine learning, his work in Convolutional neural network is often linked to Preference learning, thereby combining diverse domains of study.
His study connects Feature and Pattern recognition. Kai Yu has included themes like Inference, Cluster analysis, Probabilistic logic, Information retrieval and Collaborative filtering in his Data mining study. His studies deal with areas such as Coding and Kernel as well as Contextual image classification.
His main research concerns Genome-wide association study, Computational biology, Genetics, Internal medicine and Genetic association. The concepts of his Genome-wide association study study are interwoven with issues in Expression quantitative trait loci, Genetic predisposition and Locus. As a member of one scientific family, he mostly works in the field of Internal medicine, focusing on Endocrinology and, on occasion, Colorectal cancer, Prostate cancer, Carcinogenesis, Oncology and Cancer screening.
His Genetic association research integrates issues from SNP, Outcome, Mendelian Randomization Analysis and Heritability. Kai Yu studied SNP and Correlation and dependence that intersect with Data mining. Many of his research projects under Single-nucleotide polymorphism are closely connected to Nasopharyngeal carcinoma with Nasopharyngeal carcinoma, tying the diverse disciplines of science together.
His scientific interests lie mostly in Genome-wide association study, Internal medicine, Computational biology, Cancer and Summary data. His Genome-wide association study study introduces a deeper knowledge of Genetics. In his study, Candidate gene, Cancer screening, Oncology, Carcinogenesis and Prostate cancer is strongly linked to Endocrinology, which falls under the umbrella field of Internal medicine.
The study incorporates disciplines such as Single-nucleotide polymorphism, Linkage disequilibrium and Genetic association in addition to Computational biology. The Single-nucleotide polymorphism study combines topics in areas such as Quantitative trait locus, Cancer research and In silico. Kai Yu has researched Cancer in several fields, including Lung cancer and Hazard ratio.
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3D Convolutional Neural Networks for Human Action Recognition
Shuiwang Ji;Wei Xu;Ming Yang;Kai Yu.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2013)
Locality-constrained Linear Coding for image classification
Jinjun Wang;Jianchao Yang;Kai Yu;Fengjun Lv.
computer vision and pattern recognition (2010)
Linear spatial pyramid matching using sparse coding for image classification
Jianchao Yang;Kai Yu;Yihong Gong;Thomas Huang.
computer vision and pattern recognition (2009)
Bidirectional LSTM-CRF Models for Sequence Tagging
Zhiheng Huang;Wei Xu;Kai Yu.
arXiv: Computation and Language (2015)
Nonlinear Learning using Local Coordinate Coding
Kai Yu;Tong Zhang;Yihong Gong.
neural information processing systems (2009)
Image classification using super-vector coding of local image descriptors
Xi Zhou;Kai Yu;Tong Zhang;Thomas S. Huang.
european conference on computer vision (2010)
Large-scale image classification: Fast feature extraction and SVM training
Yuanqing Lin;Fengjun Lv;Shenghuo Zhu;Ming Yang.
computer vision and pattern recognition (2011)
Learning Gaussian processes from multiple tasks
Kai Yu;Volker Tresp;Anton Schwaighofer.
international conference on machine learning (2005)
Communication Efficient Distributed Machine Learning with the Parameter Server
Mu Li;David G Andersen;Alex J Smola;Kai Yu.
neural information processing systems (2014)
Probabilistic memory-based collaborative filtering
Kai Yu;A. Schwaighofer;V. Tresp;Xiaowei Xu.
IEEE Transactions on Knowledge and Data Engineering (2004)
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