Home > Search Results: keywords:"deep belief networks|restricted Boltzmann machines|knowledge extraction|knowledge representation|pruning|architecture optimization"
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National Repository of Grey Literature 1 records found  Search took 0.01 seconds. 
Knowledge Extraction with Deep Belief Networks
 Bronec, Jan ; Mrázová, Iveta (advisor) ; Červíčková, Věra (referee)
Deep Belief Networks (DBNs) are multi-layered neural networks constructed as a series of Restricted Boltzmann Machines stacked on each other. Like several other types of neural networks, increasing the size of a DBN will generally improve its performance. However, this comes at the cost of increased computational complexity and memory requirements. It is usually necessary to reduce a deep neural network's size to deploy it on a mobile device. To address this issue, we focus on a size-reduction technique called pruning. Pruning aims to zero out a large portion of the network's weights without significantly affecting its accuracy. We apply selected pruning algorithms to DBNs and evaluate their performance on both grayscale and color images. We also investigate the performance of the so-called confidence rules extracted from a trained DBN. These rules offer a knowledge representation that is easy to interpret. We investigate whether they also provide an accurate low-cost alternative to the original network. 1
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