Latent structure
受限玻尔tzmann机 (RBM)
受限玻尔tzmann机 (Restricted Boltzmann Machine, RBM) 是一种两层生成概率模型，由可见单元（观测单元）和隐藏单元（潜在单元）组成，它们通过一个无向二分图连接，层内无连接。该模型最初由 Paul Smolensky 于 1986 年以“Harmonium”之名提出，后由 Geoffrey Hinton 和 Ruslan Salakhutdinov 在其 2006 年发表于《Science》杂志的开创性论文中得到有力复兴。RBM 曾是深度信念网络 (Deep Belief Network, DBN) 贪婪层级预训练的基石，在深度神经网络经历多年停滞后，重新点燃了人们对其的兴趣。
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来源
- Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the Dimensionality of Data with Neural Networks. Science, 313(5786), 504–507. DOI: 10.1126/science.1127647 ↗
- Hinton, G. E. (2002). Training Products of Experts by Minimizing Contrastive Divergence. Neural Computation, 14(8), 1771–1800. DOI: 10.1162/089976602760128018 ↗
- Smolensky, P. (1986). Information Processing in Dynamical Systems: Foundations of Harmony Theory. In D. E. Rumelhart & J. L. McClelland (Eds.), Parallel Distributed Processing, Vol. 1 (pp. 194–281). MIT Press. ISBN: 978-0-262-68053-0
- Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning (Ch. 20). MIT Press. ISBN: 978-0-262-03561-3
如何引用本页
ScholarGate. (2026, June 3). Restricted Boltzmann Machine (RBM) — Bipartite Generative Energy Model. ScholarGate. https://scholargate.app/zh/deep-learning/restricted-boltzmann-machine
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