Deep Belief Networks

By Geoffrey I. Webb, Johannes Fürnkranz, Geoffrey E. Hinton, Claude Sammut, J. Sander, Michail Vlachos, Yee Whye Teh, Ying Yang, D. Mladení, Janez Brank and 6 more. Encyclopedia of Machine Learning, 2010

380 citations recorded by the source corpus.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/f6f80f4d7af095a5dd8742f1a352c4d2dbc9b50d

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In Artificial Intelligence

Citations in the corpus

A Comprehensive Overview and Comparative Analysis on Deep Learning Models: CNN, RNN, LSTM, GRU (2023, methodology): “Various generative models have been developed and applied in different contexts, such as Auto-Encoder [48], Generative Adversarial Network (GAN) [33], Restricted Boltzmann Machine (RBM) [49], Deep Belief Network (DBN) [50], and Self-Organizing Map (SOM) [51].”
The minority report: some common assumptions to reconsider in the modelling of the brain and behaviour (2016, background): “…blueprint are possible (e.g. convolutional feature extraction (LeCun & Bengio, 1995) and extension to sequential data (Sutskever & Hinton, 2007)); see (Hinton, 2009) for a concise overview, (Bengio, 2009) for a tutorial introduction and (Schmidhuber, 2015) for an exhaustive history and references.”
Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions (2021, background): “A Deep Belief Network (DBN) [40] is a multi-layer generative graphical model of stacking several individual unsupervised networks such as AEs or RBMs, that use each network’s hidden layer as the input for the next layer, i.e, connected sequentially.”
State-of-the-art review on deep learning in medical imaging. (2019, background): “The rigid DBN-based system consists of three DBNs each trained on different priors (systole, diastole and non-LV).”
Deep Learning for Medical Anomaly Detection – A Survey (2020, methodology): “They retrieve a multi-modal feature vector by concatenating the top-level hidden unit activations of the multimodal DBN.”
Intelligent Buildings of the Future: Cyberaware, Deep Learning Powered, and Human Interacting (2016, methodology): “RBMs are the building blocks of deep belief networks (DBNs) [32] and have been used in a range of domains, including image processing and time series data modeling.”
Deep Learning and Its Applications in Biomedicine (2018, methodology): “P P However, the p ð h Þ in the formula p ð v Þ ¼ h p ð v ; h Þ ¼ h p ð h Þ p ð v = h Þ is replaced by a better model after the weight of connections W is learned by an RBM [3,100].”
SySeVR: A Framework for Using Deep Learning to Detect Software Vulnerabilities (2018, methodology): “Deep learning — including Recurrent Neural Networks (RNN) [12], [13], [14], [15], [16], Convolutional Neural Networks (CNN) [17], [18], and Deep Belief Networks (DBN) [19], [20] — has been successful in image and natural language processing.”
Machine Learning and Deep Learning Methods for Cybersecurity (2018, background): “The concept of DL was proposed by Hinton [15] based on the deep belief network (DBN), in which an unsupervised greedy layer-by-layer training algorithm is proposed that provides hope for solving the optimization problem of deep structure.”
Exploiting the deep learning paradigm for recognizing human actions (2014, background): “The two layers are connected each other by means of the weight matrix W and there are no connections within a layer [11].”
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks (2019, background): “The simulations conducted using the IEEE 118-bus test system and the IEEE 300-bus test system showed that the CDBN aided FDI detection scheme was resilient to the environmental noise and had a higher detection accuracy than its SVM aided counterparts.”
EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks (2020, methodology): “For the comparison purpose, we also include the experimental results of [19] with deep belief networks (DBN) [55] and support vector machine (SVM) [56] in Table 2.”