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- W2912172260 abstract "Unsupervised neural nets such as Restricted Boltzmann Machines(RBMs) and Deep Belif Networks(DBNs), are powerful in automatic feature extraction,unsupervised weight initialization and density estimation. In this paper,we demonstrate that the parameters of these neural nets can be dramatically reduced without affecting their performance. We describe a method to reduce the parameters required by RBM which is the basic building block for deep architectures. Further we propose an unsupervised sparse deep architectures selection algorithm to form sparse deep neural networks.Experimental results show that there is virtually no loss in either generative or discriminative performance." @default.
- W2912172260 created "2019-02-21" @default.
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- W2912172260 date "2019-01-21" @default.
- W2912172260 modified "2023-09-27" @default.
- W2912172260 title "On Compression of Unsupervised Neural Nets by Pruning Weak Connections" @default.
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- W2912172260 doi "https://doi.org/10.48550/arxiv.1901.07066" @default.
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