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Dynamical analysis of contrastive divergence learning: Restricted Boltzmann machines with Gaussian visible units
Affiliation:1. Department of Complexity Science and Engineering, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa-shi, Chiba 277-8561, Japan;2. RIKEN Brain Science Institute, 2-1 Hirosawa, Wako-shi, Saitama 351-0198, Japan;1. School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China;2. Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China, Wuhan 430074, China;3. Department of Computer Science, College of Science and Engineering, City University of Hong Kong, Kowloon Tong, Hong Kong;1. Department of Computer Science, Faculty of Mathematics and Computer Science, South Asian University, Delhi, India;2. Department of Mathematics, Indian Institute of Technology, Delhi, India;1. Escuela Superior de Tizayuca, Universidad Autonoma del Estado de Hidalgo, Tizayuca, Hidalgo, Mexico;2. Centro de Investigacion en Computacion, Instituto Politecnico Nacional, Mexico City, Mexico;3. Unidad Profesional Interdisciplinaria de Biotecnologia, Instituto Politecnico Nacional, Mexico City, Mexico
Abstract:
Keywords:Deep learning  Restricted Boltzmann machine  Contrastive divergence  Component analysis  Stability of learning algorithms
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