Results 21 to 30 of about 114,287 (303)
To Prune or not to Prune: A Chaos-Causality Approach to Principled Pruning of Dense Neural Networks
CoRR, 2023 Reducing the size of a neural network (pruning) by removing weights without impacting its performance is an important problem for resource-constrained devices. In the past, pruning was typically accomplished by ranking or penalizing weights based on criteria like magnitude and removing low-ranked weights before retraining the remaining ones.
Rajan Sahu +4 more
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The role of pruning in the intensification of plum production
International Journal of Horticultural Science, 2006 In an orchard planted in the spring of 1997, four kinds of spacing have been applied (4.0 m x 1.5 m, 4.0 m x 2.0 m, 5.0 m x 2.5 in and 6.0 m x 3.0 m). Four cultivars (‘Cacanska lepotica', Stanley' ‘Bluefre' and ‘President') grafted on Myrobalan rootstock
I. Gonda
doaj +1 more source
Absorption Pruning of Deep Neural Network for Object Detection in Remote Sensing Imagery
Remote Sensing, 2022 In recent years, deep convolutional neural networks (DCNNs) have been widely used for object detection tasks in remote sensing images. However, the over-parametrization problem of DCNNs hinders their application in resource-constrained remote sensing ...
Jielei Wang +4 more
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ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression [PDF]
IEEE International Conference on Computer Vision, 2017 We propose an efficient and unified framework, namely ThiNet, to simultaneously accelerate and compress CNN models in both training and inference stages.
Jian-Hao Luo, Jianxin Wu, Weiyao Lin
semanticscholar +1 more source
HRank: Filter Pruning Using High-Rank Feature Map [PDF]
Computer Vision and Pattern Recognition, 2020 Neural network pruning offers a promising prospect to facilitate deploying deep neural networks on resource-limited devices. However, existing methods are still challenged by the training inefficiency and labor cost in pruning designs, due to missing ...
Mingbao Lin +6 more
semanticscholar +1 more source
To prune, or not to prune: exploring the efficacy of pruning for model compression
CoRR, 2017 Model pruning seeks to induce sparsity in a deep neural network's various connection matrices, thereby reducing the number of nonzero-valued parameters in the model. Recent reports (Han et al., 2015; Narang et al., 2017) prune deep networks at the cost of only a marginal loss in accuracy and achieve a sizable reduction in model size.
Michael Zhu, Suyog Gupta
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Jmax-pruning: A facility for the information theoretic pruning of modular classification rules. [PDF]
, 2012 The Prism family of algorithms induces modular classification rules in contrast to the Top Down Induction of Decision Trees (TDIDT) approach which induces classification rules in the intermediate form of a tree structure.
Frederic Stahl +5 more
core +1 more source
Mathematics, 2023 Among various network compression methods, network pruning has developed rapidly due to its superior compression performance. However, the trivial pruning threshold limits the compression performance of pruning.
Yunlong Ding, Di-Rong Chen
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Effect of mechanical pruning on the yield and quality of ‘Fortune’ mandarins
Spanish Journal of Agricultural Research, 2014 This work compares mechanical pruning followed up by hand pruning versus manual pruning in the case of ‘Fortune’ mandarins. Yield and fruit quality were measured over a three-year period.
Bernardo Martin-Gorriz +2 more
doaj +1 more source
What to Prune and What Not to Prune at Initialization
CoRR, 2022 Post-training dropout based approaches achieve high sparsity and are well established means of deciphering problems relating to computational cost and overfitting in Neural Network architectures. Contrastingly, pruning at initialization is still far behind.
openaire +2 more sources