方法证据记录
Restricted Boltzmann Machine
A Restricted Boltzmann Machine is a two-layer generative probabilistic model consisting of visible (observed) and hidden (latent) binary units connected by an undirected bipartite graph with no within-layer connections. Originally introduced as the 'Harmonium' by Paul Smolensky in 1986 and powerfully revived by Geoffrey Hinton and Ruslan Salakhutdinov in their landmark 2006 Science paper, RBMs became historically pivotal as the building block for greedy layer-wise pre-training of Deep Belief Networks, restarting interest in deep neural networks after years of stagnation.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Restricted Boltzmann Machine (RBM) — Bipartite Generative Energy Model
分类方法记录 · latent-structure / deep-learning
- 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
精选声明
声明已持久化到证据分类账中，每个声明都有自己的评估。
尚无精选声明
当分类账中没有声明时，此视图不会自行创建声明评估。
相关方法
从方法图中生成，显示为机器建议的关系 — 不推断任何证据声明。