In this work we present a method to improve the pruning step of the current state-of-the-art methodology to compress neural networks. The novelty of the proposed pruning technique is in its differentiability, which allows pruning to be performed during the backpropagation phase of the network training. This enables an end-to-end learning and strongly reduces the training time. The technique is ...

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...

In this paper, we propose a Runtime Neural Pruning (RNP) framework which prunes the deep neural network dynamically at the runtime. Unlike existing neural pruning methods which produce a fixed pruned model for deployment, our method preserves the full ability of the original network and conducts pruning according to the input image and current feature maps adaptively. The pruning is performed i...

The cost-complexity pruning generates nested subtrees and selects the best one. However, its computational cost is large since it uses hold-out sample or crossvalidation. On the other hand, the pruning algorithms based on posterior calculations such as BIC (MDL) and MEP are faster, but they sometimes produce too big or small trees to yield poor generalization errors. In this paper, we propose a...

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...

We introduce second-order methods for training and pruning of general Boltzmann networks trained with cross-entropy error. In particular, we derive the second derivatives for the entropic cost function. We illustrate pruning on Boltzmann zippers, applied to real-world data | a speechreading (lipreading) problem.

This paper presents two techniques for language model (LM) adaptation. The first aims to build a more general LM. We propose a distribution-based pruning of n-gram LMs, where we prune n-grams that are likely to be infrequent in a new document. Experimental results show that the distribution-based pruning method performed up to 9% (word perplexity reduction) better than conventional cutoff metho...