A graphical representation of an example Boltzmann machine. Each undirected edge represents dependency. In this example there are 3 hidden units and 4 visible units. This is not a restricted Boltzmann machine.
A Boltzmann machine (also called Sherrington–Kirkpatrick model with external field or stochastic Ising–Lenz–Little model), named after Ludwig Boltzmann is a stochastic spin-glass model with an external field, i.e., a Sherrington–Kirkpatrick model,[1] that is a stochastic Ising model. It is a statistical physics technique applied in the context of cognitive science.[2] It is also classified as a Markov random field.[3]
Boltzmann machines are theoretically intriguing because of the locality and Hebbian nature of their training algorithm (being trained by Hebb's rule), and because of their parallelism and the resemblance of their dynamics to simple physical processes. Boltzmann machines with unconstrained connectivity have not been proven useful for practical problems in machine learning or inference, but if the connectivity is properly constrained, the learning can be made efficient enough to be useful for practical problems.[4]
They are named after the Boltzmann distribution in statistical mechanics, which is used in their sampling function. They were heavily popularized and promoted by Geoffrey Hinton, Terry Sejnowski and Yann LeCun in cognitive sciences communities, particularly in machine learning,[2] as part of "energy based models" (EBM), because Hamiltonians of spin glasses as energy are used as a starting point to define the learning task.[5]
^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
^ abAckley, David H.; Hinton, Geoffrey E.; Sejnowski, Terrence J. (1985). "A Learning Algorithm for Boltzmann Machines" (PDF). Cognitive Science. 9 (1): 147–169. doi:10.1207/s15516709cog0901_7. Archived from the original (PDF) on 18 July 2011.
^Osborn, Thomas R. (1 January 1990). "Fast Teaching of Boltzmann Machines with Local Inhibition". International Neural Network Conference. Springer Netherlands. pp. 785. doi:10.1007/978-94-009-0643-3_76. ISBN 978-0-7923-0831-7.
^Nijkamp, E.; Hill, M. E; Han, T. (2020), "On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models", Proceedings of the AAAI Conference on Artificial Intelligence, 4 (34): 5272–5280, arXiv:1903.12370, doi:10.1609/aaai.v34i04.5973
Boltzmannmachine (also called Sherrington–Kirkpatrick model with external field or stochastic Ising–Lenz–Little model), named after Ludwig Boltzmann...
A restricted Boltzmannmachine (RBM) (also called a restricted Sherrington–Kirkpatrick model with external field or restricted stochastic Ising–Lenz–Little...
In statistical mechanics and mathematics, a Boltzmann distribution (also called Gibbs distribution) is a probability distribution or probability measure...
multimodal. A Boltzmannmachine is a type of stochastic neural network invented by Geoffrey Hinton and Terry Sejnowski in 1985. Boltzmannmachines can be seen...
activation state. In Boltzmannmachines, it plays the role of the Cost function. This analogy with physics is inspired by Ludwig Boltzmann's analysis of a gas'...
quantum restricted Boltzmannmachine. Inspired by the success of Boltzmannmachines based on classical Boltzmann distribution, a new machine learning approach...
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a composition of simple, unsupervised networks such as restricted Boltzmannmachines (RBMs) or autoencoders, where each sub-network's hidden layer serves...
In machine learning, support vector machines (SVMs, also support vector networks) are supervised max-margin models with associated learning algorithms...
units). Boltzmannmachine learning was at first slow to simulate, but the contrastive divergence algorithm speeds up training for Boltzmannmachines and Products...
images. In International Conference on Machine Learning (pp. 432-440). Cho, Kyunghyun (2013). "BoltzmannMachines and Denoising Autoencoders for Image Denoising"...
Supervised learning (SL) is a paradigm in machine learning where input objects (for example, a vector of predictor variables) and a desired output value...
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...
In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from...
of an object within a field). Autoencoder Boltzmannmachine Hopfield network Restricted Boltzmannmachine Peter, Dayan; Hinton, Geoffrey E.; Neal, Radford...
features have been introduced, based on Convolutional Gated Restricted BoltzmannMachines and Independent Subspace Analysis. It's Application can be seen in...
biology, physics, mathematics, and engineering. He co-invented the Boltzmannmachine with Geoffrey Hinton and pioneered the application of learning algorithms...
is the final low-dimensional feature or representation. Restricted Boltzmannmachines (RBMs) are often used as a building block for multilayer learning...
Co-training Transduction Deep learning Deep belief networks Deep Boltzmannmachines Deep Convolutional neural networks Deep Recurrent neural networks...
pass, rather than multiple passes through the network. Compared to Boltzmannmachines and linear ICA, there is no restriction on the type of function used...
Epileptic Seizure Detection with Stacked Convolutional Restricted BoltzmannMachine”, IETE Journal of Research, (July 30, 2023) Aruloli, M. (24 May 2011)...
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