Structural neural network pruning aims to remove the redundant channels in deep convolutional networks (CNNs) by filters of less importance final output accuracy. To reduce degradation performance after pruning, many methods utilize loss with sparse regularization produce structured sparsity. In this paper, we analyze these sparsity-training-based and find that unpruned is unnecessary. Moreover...

in order to investigate the effect of pruning and potassium nutrition on pumpkin grain yield and quality, a factorial experiment based on complete block design with four replications was carried out in nooshar village of ardabil province, ardabil, iran, in 2007. experimental factors include potassium nutrition in three level (0, 75 and 150 kg/ha from potassium sulfate) and stem pruning (without...

pomegranate is one of the major horticultural crops in iran, but studies about this product; especially its pruning is very little. therefore this study was carried out to assess the effect of pruning and nutrition on pomegranate cultivar “robab” in qaemieh, fars province during 2008-2009. this research was conducted factorial experiment based on randomized complete block design (rcbd) with 5 p...

The rise of neural network (NN) applications has prompted an increased interest in compression, with a particular focus on channel pruning, which does not require any additional hardware. Most pruning methods employ either single-layer operations or global schemes to determine channels remove followed by fine-tuning the network. In this paper we present Gator, channel-pruning method temporarily...

summer pruning and calcium spray could improve kiwifruits quality. in this study, effect of summer pruning in two times, 2 and 15 days after petal fall alone or in combination with %1 calcium chloride spray on fruit quality and mineral elements contents of ‘hayward’ kiwifruit was investigated. calcium sprayed four times 90 days after petal fall with 15 days intervals. the results showed that su...

We address the problem of tight XML schemas and propose regular tree automata to model XML data. We show that the tree automata model is more powerful that the XML DTDs and is closed under main algebraic operations. We introduce the XML query algebra based the tree automata model, and discuss the query optimization and query pruning techniques. Finally, we show the conversion of tree automata s...

We present two additions to the hierarchical mixture of experts (HME) architecture. By applying a likelihood splitting criteria to each expert in the HME we "grow" the tree adaptively during training. Secondly, by considering only the most probable path through the tree we may "prune" branches away, either temporarily, or permanently if they become redundant . We demonstrate results for the gro...