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A restricted Boltzmann machine (RBM) (also called a restricted Sherrington–Kirkpatrick model with external field or restricted stochastic Ising–Lenz–Little model) is a generative stochastic artificial neural network that can learn a probability distribution over its set of inputs.[1]
RBMs were initially proposed under the name Harmonium by Paul Smolensky in 1986,[2] and rose to prominence after Geoffrey Hinton and collaborators used fast learning algorithms for them in the mid-2000s. RBMs have found applications in dimensionality reduction,[3] classification,[4] collaborative filtering,[5] feature learning,[6] topic modelling[7], immunology[8], and even many‑body quantum mechanics.[9][10] They can be trained in either supervised or unsupervised ways, depending on the task.[citation needed]
As their name implies, RBMs are a variant of Boltzmann machines, with the restriction that their neurons must form a bipartite graph:
a pair of nodes from each of the two groups of units (commonly referred to as the "visible" and "hidden" units respectively) may have a symmetric connection between them; and
there are no connections between nodes within a group.
By contrast, "unrestricted" Boltzmann machines may have connections between hidden units. This restriction allows for more efficient training algorithms than are available for the general class of Boltzmann machines, in particular the gradient-based contrastive divergence algorithm.[11]
Restricted Boltzmann machines can also be used in deep learning networks. In particular, deep belief networks can be formed by "stacking" RBMs and optionally fine-tuning the resulting deep network with gradient descent and backpropagation.[12]
^Sherrington, David; Kirkpatrick, Scott (1975), "Solvable Model of a Spin-Glass", Physical Review Letters, 35 (35): 1792–1796, Bibcode:1975PhRvL..35.1792S, doi:10.1103/PhysRevLett.35.1792
^Smolensky, Paul (1986). "Chapter 6: Information Processing in Dynamical Systems: Foundations of Harmony Theory" (PDF). In Rumelhart, David E.; McLelland, James L. (eds.). Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Volume 1: Foundations. MIT Press. pp. 194–281. ISBN 0-262-68053-X.
^Hinton, G. E.; Salakhutdinov, R. R. (2006). "Reducing the Dimensionality of Data with Neural Networks" (PDF). Science. 313 (5786): 504–507. Bibcode:2006Sci...313..504H. doi:10.1126/science.1127647. PMID 16873662. S2CID 1658773. Archived from the original (PDF) on 2015-12-23. Retrieved 2015-12-02.
^Larochelle, H.; Bengio, Y. (2008). Classification using discriminative restricted Boltzmann machines(PDF). Proceedings of the 25th international conference on Machine learning - ICML '08. p. 536. doi:10.1145/1390156.1390224. ISBN 978-1-60558-205-4.
^Salakhutdinov, R.; Mnih, A.; Hinton, G. (2007). Restricted Boltzmann machines for collaborative filtering. Proceedings of the 24th international conference on Machine learning - ICML '07. p. 791. doi:10.1145/1273496.1273596. ISBN 978-1-59593-793-3.
^Coates, Adam; Lee, Honglak; Ng, Andrew Y. (2011). An analysis of single-layer networks in unsupervised feature learning(PDF). International Conference on Artificial Intelligence and Statistics (AISTATS). Archived from the original (PDF) on 2014-12-20. Retrieved 2014-12-19.
^Ruslan Salakhutdinov and Geoffrey Hinton (2010). Replicated softmax: an undirected topic model Archived 2012-05-25 at the Wayback Machine. Neural Information Processing Systems23.
^Bravi, Barbara; Di Gioacchino, Andrea; Fernandez-de-Cossio-Diaz, Jorge; Walczak, Aleksandra M; Mora, Thierry; Cocco, Simona; Monasson, Rémi (2023-09-08). Bitbol, Anne-Florence; Eisen, Michael B (eds.). "A transfer-learning approach to predict antigen immunogenicity and T-cell receptor specificity". eLife. 12: e85126. doi:10.7554/eLife.85126. ISSN 2050-084X. PMC 10522340. PMID 37681658.
^Carleo, Giuseppe; Troyer, Matthias (2017-02-10). "Solving the quantum many-body problem with artificial neural networks". Science. 355 (6325): 602–606. arXiv:1606.02318. Bibcode:2017Sci...355..602C. doi:10.1126/science.aag2302. ISSN 0036-8075. PMID 28183973. S2CID 206651104.
^Melko, Roger G.; Carleo, Giuseppe; Carrasquilla, Juan; Cirac, J. Ignacio (September 2019). "Restricted Boltzmann machines in quantum physics". Nature Physics. 15 (9): 887–892. Bibcode:2019NatPh..15..887M. doi:10.1038/s41567-019-0545-1. ISSN 1745-2481. S2CID 256704838.
^Miguel Á. Carreira-Perpiñán and Geoffrey Hinton (2005). On contrastive divergence learning. Artificial Intelligence and Statistics.
A restrictedBoltzmannmachine (RBM) (also called a restricted Sherrington–Kirkpatrick model with external field or restricted stochastic Ising–Lenz–Little...
Boltzmannmachine (also called Sherrington–Kirkpatrick model with external field or stochastic Ising–Lenz–Little model), named after Ludwig Boltzmann...
exponential to the size of the machine[citation needed]. A more efficient architecture is called restrictedBoltzmannmachine where connection is only allowed...
layer is the final low-dimensional feature or representation. RestrictedBoltzmannmachines (RBMs) are often used as a building block for multilayer learning...
viewed as a composition of simple, unsupervised networks such as restrictedBoltzmannmachines (RBMs) or autoencoders, where each sub-network's hidden layer...
April 2024. Hinton, G. (2012). "A Practical Guide to Training RestrictedBoltzmannMachines" (PDF). Neural Networks: Tricks of the Trade. Lecture Notes...
method involves treating each neighboring set of two layers as a restrictedBoltzmannmachine so that pretraining approximates a good solution, then using...
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn...
layers of binary or real-valued latent variables. It uses a restrictedBoltzmannmachine to model each new layer of higher level features. Each new layer...
of an object within a field). Autoencoder Boltzmannmachine Hopfield network RestrictedBoltzmannmachine Peter, Dayan; Hinton, Geoffrey E.; Neal, Radford...
as did Professor Anton Van Den Hengel of the Australian Institute for Machine Learning. In December 2022, the question and answer website Stack Overflow...
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features have been introduced, based on Convolutional Gated RestrictedBoltzmannMachines and Independent Subspace Analysis. It's Application can be seen...
machine (JVM). It is a framework with wide support for deep learning algorithms. Deeplearning4j includes implementations of the restrictedBoltzmann machine...
already by Frank Rosenblatt in his book Perceptron. This extreme learning machine was not yet a deep learning network. In 1965, the first deep-learning feedforward...
Chief Scientist and co-founder: Ilya Sutskever, a former Google expert on machine learning Chief Technology Officer: Mira Murati, previously at Leap Motion...
pass, rather than multiple passes through the network. Compared to Boltzmannmachines and linear ICA, there is no restriction on the type of function used...
Supervised learning (SL) is a paradigm in machine learning where input objects (for example, a vector of predictor variables) and a desired output value...
In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from...
"Rectified Linear Units Improve RestrictedBoltzmannMachines", 27th International Conference on International Conference on Machine Learning, ICML'10, USA: Omnipress...