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ISSN: 2574 -1241
ISSN: 2574 -1241
Received: August 22, 2017; Published: September 08, 2017
DOI: 10.26717/BJSTR.2017.01.000336
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|
International Association of Landscape Archaeology, Czech Glass Society, Czech Republic
Department of Chemistry, Semenov Institute of Chemical Physics, USSR Academy of Sciences, Moscow, Russia
Pharmaco-kinetics, dynamics and Drug Metabolism, Touro College of Pharmacy, USA
Professor of Nuclear Medicine, Faculty of Medicine and Surgery, University of Milan, Milan, Italy
Associate Professor at Department of Breast and Thyorid Surgey, Chongqing General Hospital, China
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