Artificial intelligence, Algorithm, Image texture, Pattern recognition and Computer vision are his primary areas of study. His Artificial intelligence study frequently draws connections to other fields, such as Random field. The study incorporates disciplines such as Gibbs sampling, Probabilistic logic, Bioinformatics and Expectation–maximization algorithm in addition to Algorithm.
His Gibbs sampling study integrates concerns from other disciplines, such as Probability distribution and Markov chain Monte Carlo. The various areas that Ying Nian Wu examines in his Pattern recognition study include Convolution and Autoencoder. In general Computer vision study, his work on Image processing, Texture filtering and Texture compression often relates to the realm of Basis, thereby connecting several areas of interest.
His primary scientific interests are in Artificial intelligence, Pattern recognition, Algorithm, Convolutional neural network and Markov chain Monte Carlo. His studies deal with areas such as Machine learning and Computer vision as well as Artificial intelligence. His Pattern recognition research integrates issues from Object, Statistical model and Random field.
The study incorporates disciplines such as Markov random field and Principle of maximum entropy in addition to Random field. His Algorithm research incorporates themes from Inference, Maximum likelihood, Generator, Markov chain and Function. The concepts of his Convolutional neural network study are interwoven with issues in Question answering, Visualization, Training set and Benchmark.
His primary areas of study are Artificial intelligence, Algorithm, Function, Markov chain Monte Carlo and Pattern recognition. His study on Artificial intelligence is mostly dedicated to connecting different topics, such as Energy. His work deals with themes such as Maximum likelihood, Generator, Prior probability and Divergence, which intersect with Algorithm.
Ying Nian Wu usually deals with Function and limits it to topics linked to Iterative method and Solver and Langevin dynamics. His biological study spans a wide range of topics, including Latent variable model, Latent variable, Sampling and Generative grammar, Generative model. His Pattern recognition research includes elements of Effective method, Graph Node, Graph and Benchmark.
The scientist’s investigation covers issues in Artificial intelligence, Energy, Function, Markov chain Monte Carlo and Algorithm. His Artificial intelligence study frequently links to other fields, such as Speech coding. His research integrates issues of Langevin dynamics, Iterative method, Anomaly detection and Markov process in his study of Function.
His Markov chain Monte Carlo research is multidisciplinary, relying on both Sampling and Machine learning, Convolutional neural network. His study in Machine learning is interdisciplinary in nature, drawing from both Space, Generative model and Code. His Algorithm study integrates concerns from other disciplines, such as Maximum likelihood and Flow.
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rMATS: Robust and flexible detection of differential alternative splicing from replicate RNA-Seq data
Shihao Shen;Juw Won Park;Zhi-xiang Lu;Lan Lin.
Proceedings of the National Academy of Sciences of the United States of America (2014)
Gianfranco Doretto;Alessandro Chiuso;Ying Nian Wu;Stefano Soatto.
International Journal of Computer Vision (2003)
A high-resolution map of active promoters in the human genome
Tae Hoon Kim;Leah O. Barrera;Ming Zheng;Chunxu Qu.
Filters, Random Fields and Maximum Entropy (FRAME): Towards a Unified Theory for Texture Modeling
Song Chun Zhu;Yingnian Wu;David Mumford.
International Journal of Computer Vision (1998)
Minimax Entropy Principle and Its Application to Texture Modeling
Song Chun Zhu;Ying Nian Wu;David Mumford.
Neural Computation (1997)
Interpretable Convolutional Neural Networks
Quanshi Zhang;Ying Nian Wu;Song-Chun Zhu.
computer vision and pattern recognition (2018)
Parameter expansion to accelerate EM: The PX-EM algorithm
Chuanhai Liu;Donald B. Rubin;Ying Nian Wu.
Parameter Expansion for Data Augmentation
Jun S. Liu;Ying Nian Wu.
Journal of the American Statistical Association (1999)
Dynamic texture recognition
P. Saisan;G. Doretto;Ying Nian Wu;S. Soatto.
computer vision and pattern recognition (2001)
S. Soatto;G. Doretto;Ying Nian Wu.
international conference on computer vision (2001)
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