Results 241 to 250 of about 6,914,944 (265)

Pruning-aware Sparse Regularization for Network Pruning

Machine Intelligence Research, 2022
Structural neural network pruning aims to remove the redundant channels in the deep convolutional neural networks (CNNs) by pruning the filters of less importance to the final output accuracy.
Nanfei Jiang, Xu Zhao, Chaoyang Zhao, Yongqi An, Ming Tang, Jinqiao Wang   +5 more
semanticscholar   +1 more source

SOKS: Automatic Searching of the Optimal Kernel Shapes for Stripe-Wise Network Pruning

IEEE Transactions on Neural Networks and Learning Systems, 2022
In spite of the remarkable performance, deep convolutional neural networks (CNNs) are typically over-parameterized and computationally expensive. Network pruning has become a popular approach to reducing the storage and calculations of CNN models, which ...
Guangzhen Liu, Ke Zhang, Meibo Lv
semanticscholar   +1 more source

FedP3: Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneity

International Conference on Learning Representations
The interest in federated learning has surged in recent research due to its unique ability to train a global model using privacy-secured information held locally on each client.
Kai Yi, Nidham Gazagnadou, Peter Richt'arik, Lingjuan Lyu   +3 more
semanticscholar   +1 more source

Adaptive Network Pruning for Wireless Federated Learning

IEEE Wireless Communications Letters, 2021
In this letter, we apply the model compression, i.e., network pruning, into wireless federated learning (FL) system to mitigate the local computation and communication bottlenecks.
Shengli Liu, Guanding Yu, Rui Yin, Jiantao Yuan   +3 more
semanticscholar   +1 more source

FALCON: FLOP-Aware Combinatorial Optimization for Neural Network Pruning

International Conference on Artificial Intelligence and Statistics
The increasing computational demands of modern neural networks present deployment challenges on resource-constrained devices. Network pruning offers a solution to reduce model size and computational cost while maintaining performance.
Xiang Meng, Wenyu Chen, Riade Benbaki, Rahul Mazumder   +3 more
semanticscholar   +1 more source

EagleEye: Fast Sub-net Evaluation for Efficient Neural Network Pruning

European Conference on Computer Vision, 2020
Finding out the computational redundant part of a trained Deep Neural Network (DNN) is the key question that pruning algorithms target on. Many algorithms try to predict model performance of the pruned sub-nets by introducing various evaluation methods ...
Bailin Li, Bowen Wu, Jiang Su, Guangrun Wang, Liang Lin   +4 more
semanticscholar   +1 more source

Variational Convolutional Neural Network Pruning

Computer Vision and Pattern Recognition, 2019
We propose a variational Bayesian scheme for pruning convolutional neural networks in channel level. This idea is motivated by the fact that deterministic value based pruning methods are inherently improper and unstable.
Chenglong Zhao, Bingbing Ni, Jian Zhang, Qiwei Zhao, Wenjun Zhang, Qi Tian   +5 more
semanticscholar   +1 more source

Pruning and quantization for deep neural network acceleration: A survey

Neurocomputing, 2021
Tailin Liang, John Glossner, Xiaotong Zhang   +2 more
exaly  

Pruning by explaining: A novel criterion for deep neural network pruning

Pattern Recognition, 2021
Seul-Ki Yeom, Philipp Seegerer, Sebastian Lapuschkin   +2 more
exaly  

Filter Sketch for Network Pruning

IEEE Transactions on Neural Networks and Learning Systems, 2022
Mingbao Lin, Liujuan Cao, Shaojie Li
exaly