A Reweighted Scheme to Improve the Representation of the Neural Autoregressive Distribution Estimator
Joint Authors
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-12-23
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
The neural autoregressive distribution estimator(NADE) is a competitive model for the task of density estimation in the field of machine learning.
While NADE mainly focuses on the problem of estimating density, the ability for dealing with other tasks remains to be improved.
In this paper, we introduce a simple and efficient reweighted scheme to modify the parameters of the learned NADE.
We make use of the structure of NADE, and the weights are derived from the activations in the corresponding hidden layers.
The experiments show that the features from unsupervised learning with our reweighted scheme would be more meaningful, and the performance of the initialization for neural networks has a significant improvement as well.
American Psychological Association (APA)
Wang, Zheng& Wu, Qingbiao. 2018. A Reweighted Scheme to Improve the Representation of the Neural Autoregressive Distribution Estimator. Computational Intelligence and Neuroscience،Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1130805
Modern Language Association (MLA)
Wang, Zheng& Wu, Qingbiao. A Reweighted Scheme to Improve the Representation of the Neural Autoregressive Distribution Estimator. Computational Intelligence and Neuroscience No. 2018 (2018), pp.1-9.
https://search.emarefa.net/detail/BIM-1130805
American Medical Association (AMA)
Wang, Zheng& Wu, Qingbiao. A Reweighted Scheme to Improve the Representation of the Neural Autoregressive Distribution Estimator. Computational Intelligence and Neuroscience. 2018. Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1130805
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1130805