Results 11 to 20 of about 22,594 (260)
Structured Pruning Algorithm with Adaptive Threshold Based on Gradient [PDF]
Jisuanji gongcheng, 2022 The network model needs to be compressed to reduce the number of model parameters and calculational cost to ensure the operation of the Deep Neural Network(DNN) model on edge equipment and real-time analysis. However, most existing pruning algorithms are
WANG Guodong, YE Jian, XIE Ying, QIAN Yueliang
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Slimmable Pruned Neural Networks
CoRR, 2022 Slimmable Neural Networks (S-Net) is a novel network which enabled to select one of the predefined proportions of channels (sub-network) dynamically depending on the current computational resource availability. The accuracy of each sub-network on S-Net, however, is inferior to that of individually trained networks of the same size due to its difficulty
Hideaki Kuratsu, Atsuyoshi Nakamura
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Frontiers in Computational Neuroscience, 2021 Neural network pruning is critical to alleviating the high computational cost of deep neural networks on resource-limited devices. Conventional network pruning methods compress the network based on the hand-crafted rules with a pre-defined pruning ratio (
Lin Chen +3 more
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Adaptive Neural Network Structure Optimization Algorithm Based on Dynamic Nodes
Current Issues in Molecular Biology, 2022 Large-scale artificial neural networks have many redundant structures, making the network fall into the issue of local optimization and extended training time.
Miao Wang +7 more
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Soft Pruning Algorithm Based on Lottery Ticket Hypothesis [PDF]
Jisuanji gongcheng, 2023 The increasing number of neural network layers exponentially increases the network complexity and limits its application scenarios.To solve this problem，this study proposes a soft pruning algorithm based on lottery ticket hypothesis.The pruning network ...
MA Jiaxiang, SONG Xiaoning
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Complex & Intelligent Systems, 2023 Neural network pruning offers great prospects for facilitating the deployment of deep neural networks on computational resource limited devices.
Hanjing Cheng +5 more
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Adaptive Pruning of Convolutional Neural Network [PDF]
Journal of Artificial Intelligence and Data Mining, 2023 Deep convolutional neural networks (CNNs) have attained remarkable success in numerous visual recognition tasks. There are two challenges when adopting CNNs in real-world applications: a) Existing CNNs are computationally expensive and memory intensive ...
S. Ahmadluei, K. Faez, B. Masoumi
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To what extent is tuned neural network pruning beneficial in software effort estimation? [PDF]
Computer Science Journal of Moldova, 2021 Software effort estimation (SEE) is of great importance for planning the budgets of future projects. The models of SEE are developed depending on the enhancements of hardware technology. However, developing such models based on neural networks remarkably
Muhammed Maruf Ozturk
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PLoS Computational Biology, 2021 During development, biological neural networks produce more synapses and neurons than needed. Many of these synapses and neurons are later removed in a process known as neural pruning.
Carolin Scholl +2 more
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Remote Sensing, 2023 The rapid development of neural networks has come at the cost of increased computational complexity. Neural networks are both computationally intensive and memory intensive; as such, the minimal energy and computing power of satellites pose a challenge ...
Penghao Xiao +4 more
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