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A Multiobjective Sparse Feature Learning Model for Deep Neural Networks

IEEE Transactions on Neural Networks and Learning Systems

Sep 5, 2015

Maoguo GongLinzhi Su

Abstract

Hierarchical deep neural networks are currently popular learning models for imitating the hierarchical architecture of human brain. Single-layer feature extractors are the bricks to build deep networks. Sparse feature learning models are popular models that can learn useful representati...read more

Mentioned in this Paper

In Silico
Biological Neural Networks
Memory Training
Reconstructive Surgical Procedures
Brain
Theoretical Study
Neural Network Simulation
Knowledge Representation (Computer)
Depth
Learning
1
73
Paper Details
References
  • References13
  • Citations8
12
  • References13
  • Citations8
1

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A Multiobjective Sparse Feature Learning Model for Deep Neural Networks

IEEE Transactions on Neural Networks and Learning Systems

Sep 5, 2015

Maoguo GongLinzhi Su

PMID: 26340790

DOI: 10.1109/tnnls.2015.2469673

Abstract

Hierarchical deep neural networks are currently popular learning models for imitating the hierarchical architecture of human brain. Single-layer feature extractors are the bricks to build deep networks. Sparse feature learning models are popular models that can learn useful representati...read more

Mentioned in this Paper

In Silico
Biological Neural Networks
Memory Training
Reconstructive Surgical Procedures
Brain
Theoretical Study
Neural Network Simulation
Knowledge Representation (Computer)
Depth
Learning
1
73

Similar Papers Found In These Feeds

Neural Networks

Here is the latest research on physiological and in silico networks and circuits implicated in the nervous system.

Related Papers

International Journal of Neural Systems

Learning feature representations with a cost-relevant sparse autoencoder

International Journal of Neural SystemsDecember 18, 2014
Martin Längkvist, Amy Loutfi
Paper Details
References
  • References13
  • Citations8
12
  • References13
  • Citations8
1
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