Learning Restricted Boltzmann Machines via Influence Maximization

Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing, Volume abs/1805.10262, 2019, Pages 828-839.

Cited by: 11|Bibtex|Views36|DOI:https://doi.org/10.1145/3313276.3316372
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Other Links: dblp.uni-trier.de|academic.microsoft.com|dl.acm.org|arxiv.org

Abstract:

Graphical models are a rich language for describing high-dimensional distributions in terms of their dependence structure. While there are algorithms with provable guarantees for learning undirected graphical models in a variety of settings, there has been much less progress in the important scenario when there are latent variables. Here ...More

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