Results 31 to 40 of about 6,914,944 (265)
Neural Network Pruning by Cooperative Coevolution [PDF]
International Joint Conference on Artificial Intelligence, 2022 Neural network pruning is a popular model compression method which can significantly reduce the computing cost with negligible loss of accuracy. Recently, filters are often pruned directly by designing proper criteria or using auxiliary modules to ...
Haopu Shang +3 more
semanticscholar +1 more source
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
doaj +1 more source
Unsupervised Adaptive Weight Pruning for Energy-Efficient Neuromorphic Systems
Frontiers in Neuroscience, 2020 To tackle real-world challenges, deep and complex neural networks are generally used with a massive number of parameters, which require large memory size, extensive computational operations, and high energy consumption in neuromorphic hardware systems ...
Wenzhe Guo +7 more
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A Verification Method on Post-Pruning Generalization Ability of Neural Network Model [PDF]
Jisuanji gongcheng, 2019 To address the over-fitting problem caused by the down-regulation of the Dropout rate in the pruning operation of the neural network model,a verification method for the generalization ability of the pruning model is proposed.By artificially occluding the
LIU Chongyang, LIU Qinrang
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Neuroinspired unsupervised learning and pruning with subquantum CBRAM arrays. [PDF]
, 2018 Resistive RAM crossbar arrays offer an attractive solution to minimize off-chip data transfer and parallelize on-chip computations for neural networks.
Jameson, John R +6 more
core +3 more sources
Auto- Train-Once: Controller Network Guided Automatic Network Pruning from Scratch [PDF]
Computer Vision and Pattern RecognitionCurrent techniques for deep neural network (DNN) pruning often involve intricate multi-step processes that re-quire domain-specific expertise, making their widespread adoption challenging.
Xidong Wu +7 more
semanticscholar +1 more source
Learning Slimming SAR Ship Object Detector Through Network Pruning and Knowledge Distillation
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 The deployment of deep convolutional neural networks (CNNs) in synthetic aperture radar (SAR) ship real-time detection is largely hindered by huge computational cost.
Shiqi Chen +3 more
semanticscholar +1 more source
Discrimination-Aware Network Pruning for Deep Model Compression [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020 We study network pruning which aims to remove redundant channels/kernels and hence speed up the inference of deep networks. Existing pruning methods either train from scratch with sparsity constraints or minimize the reconstruction error between the ...
Jing Liu +6 more
semanticscholar +1 more source
Efficient Stein Variational Inference for Reliable Distribution-lossless Network Pruning [PDF]
arXiv.org, 2022 Network pruning is a promising way to generate light but accurate models and enable their deployment on resource-limited edge devices. However, the current state-of-the-art assumes that the effective sub-network and the other superfluous parameters in ...
Yingchun Wang +6 more
semanticscholar +1 more source
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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