publication . Article . Preprint . 2016

A topological insight into restricted Boltzmann machines

Mocanu, Decebal Constantin; Mocanu, Elena; Nguyen, Phuong H.; Gibescu, Madeleine; Liotta, Antonio;
Open Access English
  • Published: 20 Apr 2016 Journal: Machine Learning, volume 104, issue 2-3, pages 243-270 (issn: 0885-6125, eissn: 1573-0565, Copyright policy)
  • Publisher: Springer Nature
Restricted Boltzmann Machines (RBMs) and models derived from them have been successfully used as basic building blocks in deep artificial neural networks for automatic features extraction, unsupervised weights initialization, but also as density estimators. Thus, their generative and discriminative capabilities, but also their computational time are instrumental to a wide range of applications. Our main contribution is to look at RBMs from a topological perspective, bringing insights from network science. Firstly, here we show that RBMs and Gaussian RBMs (GRBMs) are bipartite graphs which naturally have a small-world topology. Secondly, we demonstrate both on sy...
arxiv: Computer Science::Neural and Evolutionary Computation
free text keywords: Computer Science - Artificial Intelligence, Computer Science - Social and Information Networks, Software, Computer Science - Neural and Evolutionary Computing, Artificial Intelligence
Related Organizations
Funded by
Interoperability of Heterogeneous IoT Platforms
  • Funder: European Commission (EC)
  • Project Code: 687283
  • Funding stream: H2020 | RIA
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publication . Article . Preprint . 2016

A topological insight into restricted Boltzmann machines

Mocanu, Decebal Constantin; Mocanu, Elena; Nguyen, Phuong H.; Gibescu, Madeleine; Liotta, Antonio;