Decreasing the Size of the Restricted Boltzmann Machine

DOI10.1162/neco_a_01176zbMath1476.68238arXiv1807.02999OpenAlexW2860169200WikidataQ91588978 ScholiaQ91588978MaRDI QIDQ5154145

Takuya Kato, Yohei Saito

Publication date: 1 October 2021

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1807.02999

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Uses Software

• darch
• MNIST
• Tensor2Tensor
• WaveNet

Cites Work

• Unnamed Item
• An Efficient Learning Procedure for Deep Boltzmann Machines
• Reducing the Dimensionality of Data with Neural Networks
• Training Products of Experts by Minimizing Contrastive Divergence
• Representational Power of Restricted Boltzmann Machines and Deep Belief Networks
• Solving the quantum many-body problem with artificial neural networks
• Learning Compositional Representations of Interacting Systems with Restricted Boltzmann Machines: Comparative Study of Lattice Proteins
• A Fast Learning Algorithm for Deep Belief Nets

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