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.
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).
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.
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.
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).
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
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
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
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
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
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.