Results 251 to 260 of about 79,498 (272)

Pruned and Structurally Sparse Neural Networks

2018 IEEE MIT Undergraduate Research Technology Conference (URTC), 2018
Advances in designing and training deep neural networks have led to the principle that the large and deeper a network is, the better it can perform. As a result, computational resources have become a key limiting factor in achieving better performance.
Simon Alford, Ryan A. Robinett, Lauren Milechin, Jeremy Kepner   +3 more
openaire   +2 more sources

A new method to prune the neural network

Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium, 2000
Using the backpropagation algorithm (BP) to train neural networks is a widely adopted practice in both theory and practical applications. However, its distributed weight representation, that is the weight matrix of final network after training by using BP are usually not sparsified, and prohibits its use in the rule discovery of inherent functional ...
Weishui Wan, Kotaro Hirasawa, Jinglu Hu, Chunzhi Jin   +3 more
openaire   +1 more source

Pruning versus clipping in neural networks

Physical Review A, 1989
The number of interconnections in a neutral network is reduced by eliminating the ``weakest'' bonds. The performance is then improved by reapplying the learning algorithm.
openaire   +2 more sources

A LOCAL TRAINING AND PRUNING APPROACH FOR NEURAL NETWORKS

International Journal of Neural Systems, 2000
The training of neural networks using the extended Kalman filter (EKF) algorithm is plagued by the drawback of high computational complexity and storage requirement that may become prohibitive even for networks of moderate size. In this paper, we present a local EKF training and pruning approach that can solve this problem.
Sheng-Jiang Chang, Andrew Chi-Sing Leung, Kwok-Wo Wong, John Sum   +3 more
openaire   +2 more sources

Statistical method of pruning neural networks

IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339), 2003
A statistical method of pruning multilayer perceptrons is proposed which is expected to involve less computation than does "optimal brain surgeon". The statistical method iteratively performs the steps of estimating the error covariance of the weights, evaluating the z-statistics for the weights, pruning the weights selected by hypothesis testing, and ...
openaire   +1 more source

Pruning and quantization for deep neural network acceleration: A survey

Neurocomputing, 2021
John Glossner, Xiaotong Zhang
exaly  

GenExp: Multi-objective pruning for deep neural network based on genetic algorithm

Neurocomputing, 2021
Dong Wang
exaly  

Intermittent-Aware Neural Network Pruning

2023 60th ACM/IEEE Design Automation Conference (DAC), 2023
Chih-Chia Lin, Chia-Yin Liu, Chih-Hsuan Yen, Tei-Wei Kuo, Pi-Cheng Hsiu   +4 more
openaire   +1 more source

DMPP: Differentiable multi-pruner and predictor for neural network pruning

Neural Networks, 2022
Derong Liu, Bo Zhao
exaly  

An Improved Neural Network Pruning Technology for Automatic Modulation Classification in Edge Devices

IEEE Transactions on Vehicular Technology, 2020
Yun Lin, Ya Tu, Zheng Dou
exaly