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In machine learning, a deep belief network (DBN) is a generative graphical model, or alternatively a class of deep neural network, composed of multiple layers of latent variables ("hidden units"), with connections between the layers but not between units within each layer.
Training of the network involves a pre-training stage accomplished in a greedy layer-wise manner, similar to other deep belief networks. Depending on whether the network is to be used for discrimination or generative tasks, it is then "fine tuned" or trained with either back-propagation or the up–down algorithm (contrastive–divergence ...
Examples of deep structures that can be trained in an unsupervised manner are deep belief networks. [8] [12] The term Deep Learning was introduced to the machine learning community by Rina Dechter in 1986, [13] and to artificial neural networks by Igor Aizenberg and colleagues in 2000, in the context of Boolean threshold neurons.
A deep belief network (DBN) is a probabilistic, generative model made up of multiple hidden layers. It can be considered a composition of simple learning modules. [43] A DBN can be used to generatively pre-train a deep neural network (DNN) by using the learned DBN weights as the initial DNN weights.
A Bayesian network (also known as a Bayes network, Bayes net, belief network, or decision network) is a probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG). [1] While it is one of several forms of causal notation, causal networks are special cases of Bayesian ...
Convolutional Deep Belief Networks on CIFAR-10 [6] 21.1 August, 2010 Maxout Networks [7] 9.38: February 13, 2013: Wide Residual Networks [8] 4.0: May 23, 2016: Neural Architecture Search with Reinforcement Learning [9] 3.65: November 4, 2016: Fractional Max-Pooling [10] 3.47: December 18, 2014: Densely Connected Convolutional Networks [11] 3.46 ...
Deep Lambertian Networks (DLN) [1] is a combination of Deep belief network and Lambertian reflectance assumption which deals with the challenges posed by illumination variation in visual perception. Lambertian Reflectance model gives an illumination invariant representation which can be used for recognition.
Deep belief network; Dynamic Bayesian network; Design By Numbers; Other uses. Darebin railway station, Melbourne; DBN (band), a German dance music trio;