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A fast learning algorithm for deep belief nets

by Geoffrey E. Hinton, Simon Osindero - Neural Computation , 2006
"... We show how to use “complementary priors ” to eliminate the explaining away effects that make inference difficult in densely-connected belief nets that have many hidden layers. Using complementary priors, we derive a fast, greedy algorithm that can learn deep, directed belief networks one layer at a ..."
Abstract - Cited by 930 (51 self) - Add to MetaCart
We show how to use “complementary priors ” to eliminate the explaining away effects that make inference difficult in densely-connected belief nets that have many hidden layers. Using complementary priors, we derive a fast, greedy algorithm that can learn deep, directed belief networks one layer

3-d object recognition with deep belief nets

by Vinod Nair, Geoffrey E. Hinton - Advances in Neural Information Processing Systems 22 , 2009
"... We introduce a new type of top-level model for Deep Belief Nets and evaluate it on a 3D object recognition task. The top-level model is a third-order Boltzmann machine, trained using a hybrid algorithm that combines both generative and discriminative gradients. Performance is evaluated on the NORB d ..."
Abstract - Cited by 60 (9 self) - Add to MetaCart
We introduce a new type of top-level model for Deep Belief Nets and evaluate it on a 3D object recognition task. The top-level model is a third-order Boltzmann machine, trained using a hybrid algorithm that combines both generative and discriminative gradients. Performance is evaluated on the NORB

DEEP BELIEF NETS FOR NATURAL LANGUAGE CALL–ROUTING

by Ruhi Sarikaya, Geoffrey E. Hinton, Bhuvana Ramabhadran
"... This paper considers application of Deep Belief Nets (DBNs) to natural language call routing. DBNs have been successfully applied to a number of tasks, including image, audio and speech classification, thanks to the recent discovery of an efficient learning technique. DBNs learn a multi-layer genera ..."
Abstract - Cited by 8 (2 self) - Add to MetaCart
This paper considers application of Deep Belief Nets (DBNs) to natural language call routing. DBNs have been successfully applied to a number of tasks, including image, audio and speech classification, thanks to the recent discovery of an efficient learning technique. DBNs learn a multi

Sparse deep belief net model for visual area V2

by Chaitanya Ekanadham - Advances in Neural Information Processing Systems 20 , 2008
"... Abstract 1 Motivated in part by the hierarchical organization of the neocortex, a number of recently proposed algorithms have tried to learn hierarchical, or “deep, ” structure from unlabeled data. While several authors have formally or informally compared their algorithms to computations performed ..."
Abstract - Cited by 159 (19 self) - Add to MetaCart
some intriguing hypotheses about V2 computations. 1 This thesis is an extended version of an earlier paper by Honglak Lee, Chaitanya Ekanadham, and Andrew Ng titled “Sparse deep belief net model for visual area V2.” 1

Learning representations for multimodal data with deep belief nets

by Nitish Srivastava, Ruslan Salakhutdinov - In International Conference on Machine Learning Workshop , 2012
"... We propose a Deep Belief Network archi-tecture for learning a joint representation of multimodal data. The model defines a prob-ability distribution over the space of mul-timodal inputs and allows sampling from the conditional distributions over each data modality. This makes it possible for the mod ..."
Abstract - Cited by 4 (0 self) - Add to MetaCart
We propose a Deep Belief Network archi-tecture for learning a joint representation of multimodal data. The model defines a prob-ability distribution over the space of mul-timodal inputs and allows sampling from the conditional distributions over each data modality. This makes it possible

Document Classification using Deep Belief Nets Overview

by Lawrence Mcafee
"... This paper covers the implementation and testing of a Deep Belief Network (DBN) for the purposes of document classification. Document classification is important because of the increasing need for document organization, particularly journal articles and online information. Many popular uses of docum ..."
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This paper covers the implementation and testing of a Deep Belief Network (DBN) for the purposes of document classification. Document classification is important because of the increasing need for document organization, particularly journal articles and online information. Many popular uses

A Comparative Evaluation of Deep Belief Nets in Semi-supervised Learning

by Huixuan Tang
"... In this report I studied the performance of deep belief nets (DBNs) on semi-supervised learning problems, in which only a small proportion of data are la-beled. First the performance between DBNs and support vector machines (SVMs) are compared to investigate the advantage of deep models over shallow ..."
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In this report I studied the performance of deep belief nets (DBNs) on semi-supervised learning problems, in which only a small proportion of data are la-beled. First the performance between DBNs and support vector machines (SVMs) are compared to investigate the advantage of deep models over

Using Deep Belief Nets for Chinese Named Entity Categorization

by Yu Chen, You Ouyang, Wenjie Li, Dequan Zheng, Tiejun Zhao
"... Identifying named entities is essential in understanding plain texts. Moreover, the categories of the named entities are indicative of their roles in the texts. In this paper, we propose a novel approach, Deep Belief Nets (DBN), for the Chinese entity mention categorization problem. DBN has very str ..."
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Identifying named entities is essential in understanding plain texts. Moreover, the categories of the named entities are indicative of their roles in the texts. In this paper, we propose a novel approach, Deep Belief Nets (DBN), for the Chinese entity mention categorization problem. DBN has very

Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes

by Ruslan Salakhutdinov, Geoffrey Hintondepartment - Advances in Neural Information Processing systems (NIPS-20 , 2007
"... Abstract We show how to use unlabeled data and a deep belief net (DBN) to learn a goodcovariance kernel for a Gaussian process. We first learn a deep generative model of the unlabeled data using the fast, greedy algorithm introduced by [7]. If thedata is high-dimensional and highly-structured, a Gau ..."
Abstract - Cited by 37 (3 self) - Add to MetaCart
Abstract We show how to use unlabeled data and a deep belief net (DBN) to learn a goodcovariance kernel for a Gaussian process. We first learn a deep generative model of the unlabeled data using the fast, greedy algorithm introduced by [7]. If thedata is high-dimensional and highly-structured, a

P300 CLASSIFICATION USING DEEP BELIEF NETS Submitted by

by Amin Sobhani, Advisor Charles Anderson, Asa Ben-hur, Chris Peterson , 2014
"... P300 CLASSIFICATION USING DEEP BELIEF NETS Electroencephalogram (EEG) is measure of the electrical activity of the brain. One of the most important EEG paradigm that has been explored in BCI systems is the P300 signal. The P300 wave is an endogenous event-related-potential which can be captured duri ..."
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P300 CLASSIFICATION USING DEEP BELIEF NETS Electroencephalogram (EEG) is measure of the electrical activity of the brain. One of the most important EEG paradigm that has been explored in BCI systems is the P300 signal. The P300 wave is an endogenous event-related-potential which can be captured
Results 1 - 10 of 570,447