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| 受限玻尔tzmann机 (RBM)× | 深度信念网络 (DBN)× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族≠ | Latent structure | Machine learning |
| 起源年份≠ | 1986 | 2006 |
| 提出者≠ | Smolensky, P. (1986); popularised by Hinton, G. E. & Salakhutdinov, R. R. (2006) | Geoffrey Hinton, Simon Osindero & Yee-Whye Teh |
| 类型≠ | Generative energy-based probabilistic model | Generative probabilistic model |
| 开创性文献≠ | Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the Dimensionality of Data with Neural Networks. Science, 313(5786), 504–507. DOI ↗ | Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18(7), 1527–1554. DOI ↗ |
| 别名≠ | RBM, Harmonium, restricted Boltzmann machine, RBM generative model | DBN, Deep Generative Network, Stacked RBM Network, Derin İnanç Ağı |
| 相关 | 3 | 3 |
| 摘要≠ | 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. | A Deep Belief Network is a generative probabilistic model composed of multiple layers of stochastic, latent variables. Introduced by Hinton, Osindero, and Teh in 2006, DBNs were among the first deep architectures to be trained efficiently. Each pair of adjacent layers forms a Restricted Boltzmann Machine, and the network is trained greedily, one layer at a time, before optional supervised fine-tuning. DBNs revived interest in deep learning and demonstrated that hierarchical feature learning from raw data is tractable. |
| ScholarGate数据集 ↗ |
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