Restricted Boltzmann Machines and Their Extensions for Face Modeling
Journal Title: Biomedical Journal of Scientific & Technical Research (BJSTR) - Year 2017, Vol 1, Issue 4
Abstract
Restricted Boltzmann Machines, Deep Boltzmann Machines, and their extensions have brought much attention and become powerful tools for many machine learning tasks. The increased popularity of these techniques is not only limited in modelling static data but also time-series data. In this paper, we aim to give a review of recent developments of such models for sequential data modelling. Their structures, energy functions, learning algorithm as well as applications are also provided with systematic discussions. In this paper, we review the main structure of Restricted Boltzmann Machines and their improvements that have been proposed for data modeling. Different applications of this model are also summarized to provide a better understanding of the model. Notice that our work is different from the survey of recent developments of feature learning algorithms for time-series problems. The aim of that survey is to review unsupervised feature learning methods together with applications to several classical time-series problems. On the other hand, in this work, we also review recent trends and extensions of a specific model, i.e. the Temporal Restricted Boltzmann Machines (TRBM), for face modeling.
Authors and Affiliations
Chi Nhan Duong, Khoa Luu
Keywords
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- EP ID EP569145
- DOI 10.26717/BJSTR.2017.01.000336
- Views 186
- Downloads 0
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