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Refinements of Universal Approximation Results for Deep Belief Networks and Restricted Boltzmann Machines

JOURNAL ARTICLE published May 2011 in Neural Computation

Authors: Guido Montufar | Nihat Ay

https://doi.org/10.1162/neco_a_00113 Actions

Approximation properties of Gaussian-binary restricted Boltzmann machines and Gaussian-binary deep belief networks

JOURNAL ARTICLE published September 2022 in Neural Networks

Authors: Linyan Gu | Lihua Yang | Feng Zhou

https://doi.org/10.1016/j.neunet.2022.05.020 Actions

Representational Power of Restricted Boltzmann Machines and Deep Belief Networks

JOURNAL ARTICLE published June 2008 in Neural Computation

Authors: Nicolas Le Roux | Yoshua Bengio

https://doi.org/10.1162/neco.2008.04-07-510 Actions

Universal Approximation Depth and Errors of Narrow Belief Networks with Discrete Units

JOURNAL ARTICLE published July 2014 in Neural Computation

Authors: Guido F. Montúfar

https://doi.org/10.1162/neco_a_00601 Actions

Restricted Boltzmann Machines

BOOK CHAPTER published 2023 in Neural Networks and Deep Learning

Authors: Charu Aggarwal

https://doi.org/10.1007/978-3-031-29642-0_7 Actions

Restricted Boltzmann Machines as Models of Interacting Variables

JOURNAL ARTICLE published 16 September 2021 in Neural Computation

Authors: Nicola Bulso | Yasser Roudi

https://doi.org/10.1162/neco_a_01420 Actions

Restricted Boltzmann Machines

BOOK CHAPTER published 2018 in Neural Networks and Deep Learning

Authors: Charu C. Aggarwal

https://doi.org/10.1007/978-3-319-94463-0_6 Actions

Enhanced Gradient for Training Restricted Boltzmann Machines

JOURNAL ARTICLE published March 2013 in Neural Computation

Authors: KyungHyun Cho | Tapani Raiko | Alexander Ilin

https://doi.org/10.1162/neco_a_00397 Actions

Restricted Boltzmann Machines: an Eigencentrality-based Approach

PROCEEDINGS ARTICLE published July 2019 in 2019 International Joint Conference on Neural Networks (IJCNN)

Authors: Andrew Skabar

https://doi.org/10.1109/ijcnn.2019.8852267 Actions

Refinements of Approximation Results of Conditional Restricted Boltzmann Machines

JOURNAL ARTICLE published March 2023 in IEEE Transactions on Neural Networks and Learning Systems

Research funded by National Natural Science Foundation of China (11901113) | Guangzhou Science and Technology Plan Project (201904010225) | Guangdong Natural Science Foundation (2020A1515110951)

Authors: Linyan Gu | Lihua Yang | Feng Zhou

https://doi.org/10.1109/tnnls.2021.3105129 Actions

Learning Informative Features from Restricted Boltzmann Machines

JOURNAL ARTICLE published December 2016 in Neural Processing Letters

Research funded by Ministry of Science and Higher Education, Republic of Poland (B40020/I32)

Authors: Jakub M. Tomczak

https://doi.org/10.1007/s11063-015-9491-9 Actions

Dynamical analysis of contrastive divergence learning: Restricted Boltzmann machines with Gaussian visible units

JOURNAL ARTICLE published July 2016 in Neural Networks

Research funded by Japan Society for the Promotion of Science (JSPS) (26⋅8282)

Authors: Ryo Karakida | Masato Okada | Shun-ichi Amari

https://doi.org/10.1016/j.neunet.2016.03.013 Actions

An approach to improve online sequential extreme learning machines using restricted Boltzmann machines

PROCEEDINGS ARTICLE published July 2018 in 2018 International Joint Conference on Neural Networks (IJCNN)

Authors: Andre G. C. Pacheco | Renato A. Krohling

https://doi.org/10.1109/ijcnn.2018.8489281 Actions

Exponential Family Restricted Boltzmann Machines and Annealed Importance Sampling

PROCEEDINGS ARTICLE published July 2018 in 2018 International Joint Conference on Neural Networks (IJCNN)

Authors: Yifeng Li | Xiaodan Zhu

https://doi.org/10.1109/ijcnn.2018.8489413 Actions

On the effective initialisation for restricted Boltzmann machines via duality with Hopfield model

JOURNAL ARTICLE published November 2021 in Neural Networks

Authors: Francesca Elisa Leonelli | Elena Agliari | Linda Albanese | Adriano Barra

https://doi.org/10.1016/j.neunet.2021.06.017 Actions

On the importance of hidden bias and hidden entropy in representational efficiency of the Gaussian-Bipolar Restricted Boltzmann Machines

JOURNAL ARTICLE published September 2018 in Neural Networks

Authors: Altynbek Isabekov | Engin Erzin

https://doi.org/10.1016/j.neunet.2018.06.002 Actions

Learning Compositional Representations of Interacting Systems with Restricted Boltzmann Machines: Comparative Study of Lattice Proteins

JOURNAL ARTICLE published August 2019 in Neural Computation

Authors: Jérôme Tubiana | Simona Cocco | Rémi Monasson

https://doi.org/10.1162/neco_a_01210 Actions

Matrix Product Operator Restricted Boltzmann Machines

PROCEEDINGS ARTICLE published July 2019 in 2019 International Joint Conference on Neural Networks (IJCNN)

Authors: Cong Chen | Kim Batselier | Ching-Yun Ko | Ngai Wong

https://doi.org/10.1109/ijcnn.2019.8851877 Actions

Restricted Boltzmann machines for pre-training deep Gaussian networks

PROCEEDINGS ARTICLE published August 2013 in The 2013 International Joint Conference on Neural Networks (IJCNN)

Authors: Mark Eastwood | Chrisina Jayne

https://doi.org/10.1109/ijcnn.2013.6706918 Actions

Tensor Ring Restricted Boltzmann Machines

PROCEEDINGS ARTICLE published July 2019 in 2019 International Joint Conference on Neural Networks (IJCNN)

Authors: Maolin Wang | Chenbin Zhang | Yu Pan | Jing Xu | Zenglin Xu

https://doi.org/10.1109/ijcnn.2019.8852432 Actions
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