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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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Author: search.filters.author.Lee, Chao-HsiSubject: search.filters.subject.variational inference

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    Deep Unfolding for Topic Models
    (2018-02-01)
    Chien, Jen-Tzung
    ;
    Lee, Chao-Hsi
    ;
    電機工程學系
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    Department of Electrical and Computer Engineering
    Deep unfolding provides an approach to integrate the probabilistic generative models and the deterministic neural networks. Such an approach is benefited by deep representation, easy interpretation, flexible learning and stochastic modeling. This study develops the unsupervised and supervised learning of deep unfolded topic models for document representation and classification. Conventionally, the unsupervised and supervised topic models are inferred via the variational inference algorithm where the model parameters are estimated by maximizing the lower bound of logarithm of marginal likelihood using input documents without and with class labels, respectively. The representation capability or classification accuracy is constrained by the variational lower bound and the tied model parameters across inference procedure. This paper aims to relax these constraints by directly maximizing the end performance criterion and continuously untying the parameters in learning process via deep unfolding inference (DUI). The inference procedure is treated as the layer-wise learning in a deep neural network. The end performance is iteratively improved by using the estimated topic parameters according to the exponentiated updates. Deep learning of topic models is therefore implemented through a back-propagation procedure. Experimental results show the merits of DUI with increasing number of layers compared with variational inference in unsupervised as well as supervised topic models.
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    DEEP UNFOLDING INFERENCE FOR SUPERVISED TOPIC MODEL
    (2016)
    Lee, Chao-Hsi
    ;
    Chien, Jen-Tzung
    ;
    電機工程學系
    ;
    Department of Electrical and Computer Engineering
    Conventional supervised topic model for multi-class classification is inferred via the variational inference algorithm where the model parameters are estimated by maximizing the lower bound of the logarithm of marginal likelihood function over input documents and labels. The classification accuracy is constrained by the variational lower bound. In this study, we aim to improve the classification accuracy by relaxing this constraint through directly maximizing the negative cross entropy error function via a deep unfolding inference (DUI). The inference procedure for class posterior is treated as the layer-wise learning in a deep neural network. The classification accuracy in DUI is accordingly increased by using the estimated topic parameters according to the exponentiated updates. Deep learning of supervised topic model is achieved through an error back-propagation algorithm. Experimental results show the superiority of DUI to variational Bayes inference in supervised topic model.