*Corresponding author:
Khoa Luu, Carnegie Mellon University, Pittsburgh, Pennsylvania, USAReceived: August 22, 2017; Published: September 08, 2017
DOI: 10.26717/BJSTR.2017.01.000336
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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.
Abstract| Introduction| Conditional Restricted Boltzmann Machines| Temporal Restricted Boltzmann Machines| Deep Boltzmann Machines for Face Modeling| References|