Results 231 to 240 of about 36,522 (258)

Dynamic Pruning of CNN networks

2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA), 2019
A new, radical CNN dynamic pruning approach is presented in this paper, achieved by a new holistic intervention on both the CNN architecture and the training procedure, which targets to the parsimonious inference by learning to exploit and dynamically remove the redundant capacity of a CNN architecture.
Nikolaos Fragoulis, Ilias Theodorakopoulos, Vasileios K. Pothos, Evangelos Vassalos   +3 more
openaire   +1 more source

Network pruning via resource reallocation

Pattern Recognition
12 pages, 11 figures, 7 ...
Chen Change Loy, Zheng Ma, Yuenan Hou
exaly   +3 more sources

Optimal pruning in neural networks

Physical Review E, 2000
We study pruning strategies in simple perceptrons subjected to supervised learning. Our analytical results, obtained through the statistical mechanics approach to learning theory, are independent of the learning algorithm used in the training process.
D M, Barbato, O, Kinouchi
openaire   +2 more sources

A comprehensive review of network pruning based on pruning granularity and pruning time perspectives

Neurocomputing
Ruxin Wang
exaly   +2 more sources

Pruning in Recurrent Neural Networks

1994
Recurrent neural networks are attracting considerable interest within the neural network domain especially because of their potential in such problems as pattern completion and temporal sequence processing (Almeida, 1987; Hertz et al., 1991). As for feed-forward networks, in virtually all problems of interest the proper number of hidden units is not ...
CASTELLANO G, FANELLI A. M, PELILLO, Marcello   +2 more
openaire   +2 more sources

Pruned Neural Networks for Regression

2000
Neural networks have been widely used as a tool for regression. They are capable of approximating any function and they do not require any assumption about the distribution of the data. The most commonly used architectures for regression are the feedforward neural networks with one or more hidden layers.
Rudy Setiono, Wee Kheng Leow
openaire   +1 more source

Is Pruning Compression?: Investigating Pruning Via Network Layer Similarity

2020 IEEE Winter Conference on Applications of Computer Vision (WACV), 2020
Unstructured neural network pruning is an effective technique that can significantly reduce theoretical model size, computation demand and energy consumption of large neural networks without compromising accuracy. However, a number of fundamental questions about pruning are not answered yet.
Cody Blakeney, Yan Yan 0002, Ziliang Zong   +2 more
openaire   +1 more source

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 ...
Wan, Weishui, Hirasawa, Kotaro, Hu, Jinglu, Jin, Chunzhi   +3 more
openaire   +1 more source

Network Performance of Pruned Hierarchical Torus Network

2009 Sixth IFIP International Conference on Network and Parallel Computing, 2009
The complexity of an interconnection network often determines the size of the parallel computer and thus the attainable performance of a parallel computer is limited by the characteristics of the interconnection network. Pruning technique reduces the complexity and hence increases the performance.
M. M. Hafizur Rahman, Xiaohong Jiang 0001, Md. Shahin-Al Masud, Susumu Horiguchi   +3 more
openaire   +1 more source

Neural Network Pruning and Pruning Parameters

1996
The default multilayer neural network topology is a fully interlayer connected one. This simplistic choice facilitates the design but it limits the performance of the resulting neural networks. The best-known methods for obtaining partially connected neural networks are the so called pruning methods which are used for optimizing both the size and the ...
Thimm, Georg, Fiesler, Emile
openaire   +1 more source