Results 21 to 30 of about 22,594 (260)
Efficient Digital Predistortion Using Sparse Neural Network
IEEE Access, 2020 This paper proposes an efficient neural-network-based digital predistortion (DPD), named as envelope time-delay neural network (ETDNN) DPD. The method complies with the physical characteristics of radio-frequency (RF) power amplifiers (PAs) and uses a ...
Masaaki Tanio +2 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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Journal of Pathology Informatics, 2022 In this work, the network complexity should be reduced with a concomitant reduction in the number of necessary training examples. The focus thus was on the dependence of proper evaluation metrics on the number of adjustable parameters of the considered ...
Th.I. Götz +8 more
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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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Neural Network Pruning by Cooperative Coevolution
Proceedings of the Thirty-First 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 measure their importance, which, however, requires expertise and trial-and-error.
Haopu Shang +3 more
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Dynamically Optimizing Network Structure Based on Synaptic Pruning in the Brain
Frontiers in Systems Neuroscience, 2021 Most neural networks need to predefine the network architecture empirically, which may cause over-fitting or under-fitting. Besides, a large number of parameters in a fully connected network leads to the prohibitively expensive computational cost and ...
Feifei Zhao +6 more
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Dissecting Pruned Neural Networks
CoRR, 2019 Pruning is a standard technique for removing unnecessary structure from a neural network to reduce its storage footprint, computational demands, or energy consumption. Pruning can reduce the parameter-counts of many state-of-the-art neural networks by an order of magnitude without compromising accuracy, meaning these networks contain a vast amount of ...
Jonathan Frankle, David Bau
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Adversarial Structured Neural Network Pruning [PDF]
Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 2019 In recent years, convolutional neural networks (CNN) have been successfully employed for performing various tasks due to their high capacity. However, just like a double-edged sword, high capacity results from millions of parameters, which also brings a huge amount of redundancy and dramatically increases the computational complexity.
Xingyu Cai +3 more
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Remote Sensing, 2023 Neural network models for hyperspectral images classification are complex and therefore difficult to deploy directly onto mobile platforms. Neural network model compression methods can effectively optimize the storage space and inference time of the ...
Yu Lei +5 more
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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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