Results 11 to 20 of about 79,498 (272)
Activation-Based Pruning of Neural Networks
AlgorithmsWe present a novel technique for pruning called activation-based pruning to effectively prune fully connected feedforward neural networks for multi-object classification. Our technique is based on the number of times each neuron is activated during model
Tushar Ganguli, Edwin K. P. Chong
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Methods for Pruning Deep Neural Networks [PDF]
IEEE Access, 2022 Major revision that includes additional references and a new section for comparison of ...
Sunil Vadera, Salem Ameen
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Pruning Weightless Neural Networks
ESANN 2022 proceedings, 2022 Weightless neural networks (WNNs) are a type of machine learning model which perform prediction using lookup tables (LUTs) instead of arithmetic operations. Recent advancements in WNNs have reduced model sizes and improved accuracies, reducing the gap in accuracy with deep neural networks (DNNs).
Zachary Susskind +11 more
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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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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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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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Impact of Disentanglement on Pruning Neural Networks
CoRR, 2023 This work was funded by the Luxembourg National Research Fund (FNR) under the project reference C21/IS/15965298/ELITE.
Carl Shneider +5 more
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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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