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Deep belief network

Deep belief network

In machine learning, a deep belief network (DBN) is 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.

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DBNs can be viewed as a composition of simple, unsupervised networks such as restricted Boltzmann machines (RBMs) or autoencoders, where each sub-network's hidden layerhidden layer serves as the visible layer for the next. An RBM is an undirected, generative energy-based model with a "visible" input layer and a hidden layer and connections between but not within layers. This composition leads to a fast, layer-by-layer unsupervised training procedure, where contrastive divergence is applied to each sub-network in turn, starting from the "lowest" pair of layers (the lowest visible layer is a training set). The observation that DBNs can be trained greedily, one layer at a time, led to one of the first effective deep learning algorithms. Overall, there are many attractive implementations and uses of DBNs in real-life applications and scenarios (e.g., electroencephalography, drug discovery).

Edits on 20 Aug, 2019
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Jude Gomila approved a suggestion from Golden's AI on 20 Aug, 2019
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In machine learningmachine learning, a deep belief network (DBN) is 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. When trained on a set of examples without supervision, a DBN can learn to probabilistically reconstruct its inputs. After this learning step, a DBN can be further trained with supervision to perform classification.

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Deep belief network

In machine learning, a deep belief network (DBN) is 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.

Article

In machine learning, a deep belief network (DBN) is 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. When trained on a set of examples without supervision, a DBN can learn to probabilistically reconstruct its inputs. After this learning step, a DBN can be further trained with supervision to perform classification.

...

DBNs can be viewed as a composition of simple, unsupervised networks such as restricted Boltzmann machines (RBMs) or autoencoders, where each sub-network's hidden layer serves as the visible layer for the next. An RBM is an undirected, generative energy-based model with a "visible" input layer and a hidden layer and connections between but not within layers. This composition leads to a fast, layer-by-layer unsupervised training procedure, where contrastive divergence is applied to each sub-network in turn, starting from the "lowest" pair of layers (the lowest visible layer is a training set). The observation that DBNs can be trained greedily, one layer at a time, led to one of the first effective deep learning algorithms. Overall, there are many attractive implementations and uses of DBNs in real-life applications and scenarios (e.g., electroencephalography, drug discovery).

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Edits on 7 Aug, 2018
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Further reading

Author
Title
Link
Type

JT Turner, Adam Page, Tinoosh Mohsenin, Tim Oates

Deep Belief Networks used on High Resolution Multichannel Electroencephalography Data for Seizure Detection

http://arxiv.org/abs/1708.08430v1http://arxiv.org/abs/1708.08430v1

Academic paper

Renzhi Cao, Debswapna Bhattacharya, Jie Hou, Jianlin Cheng

DeepQA: Improving the estimation of single protein model quality with deep belief networks

http://arxiv.org/abs/1607.04379v1http://arxiv.org/abs/1607.04379v1

Academic paper

Edits on 5 Jun, 2018
Tianchang He
Tianchang He edited on 5 Jun, 2018
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Further reading (+10/-10 characters)
Further reading

Author
Title
Link
Type

JT Turner, Adam Page, Tinoosh Mohsenin, Tim Oates

Deep Belief Networks used on High Resolution Multichannel Electroencephalography Data for Seizure Detection

http://arxiv.org/abs/1708.08430v1

Academic Paperpaper

Renzhi Cao, Debswapna Bhattacharya, Jie Hou, Jianlin Cheng

DeepQA: Improving the estimation of single protein model quality with deep belief networks

http://arxiv.org/abs/1607.04379v1

Academic Paperpaper

Edits on 1 Jun, 2018
Golden AI"Merging standard tables"
Golden AI edited on 1 Jun, 2018
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Academic papers (-2 rows) (-6 cells) (-370 characters)
Further reading (+2 rows) (+8 cells) (+398 characters)
Academic papers

Author
Title
Link

JT Turner, Adam Page, Tinoosh Mohsenin, Tim Oates

Deep Belief Networks used on High Resolution Multichannel Electroencephalography Data for Seizure Detection

http://arxiv.org/abs/1708.08430v1

Renzhi Cao, Debswapna Bhattacharya, Jie Hou, Jianlin Cheng

DeepQA: Improving the estimation of single protein model quality with deep belief networks

http://arxiv.org/abs/1607.04379v1

Further reading

Author
Title
Link
Type

JT Turner, Adam Page, Tinoosh Mohsenin, Tim Oates

Deep Belief Networks used on High Resolution Multichannel Electroencephalography Data for Seizure Detection

http://arxiv.org/abs/1708.08430v1

Academic Paper

Renzhi Cao, Debswapna Bhattacharya, Jie Hou, Jianlin Cheng

DeepQA: Improving the estimation of single protein model quality with deep belief networks

http://arxiv.org/abs/1607.04379v1

Academic Paper

Edits on 13 Apr, 2018
Jude Gomila
Jude Gomila edited on 13 Apr, 2018
Academic papers

Author
Title
Link

JT Turner, Adam Page, Tinoosh Mohsenin, Tim Oates

Deep Belief Networks used on High Resolution Multichannel Electroencephalography Data for Seizure Detection

http://arxiv.org/abs/1708.08430v1

Edits on 29 Mar, 2018
Alex Dean
Alex Dean edited on 29 Mar, 2018
Academic papers

Author
Title
Link

Renzhi Cao, Debswapna Bhattacharya, Jie Hou, Jianlin Cheng

DeepQA: Improving the estimation of single protein model quality with deep belief networks

http://arxiv.org/abs/1607.04379v1

Edits on 1 Jan, 2017
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Golden AI created this topic on 1 Jan, 2017
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 Deep belief network

In machine learning, a deep belief network (DBN) is 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.

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