Publication date
October 2021
DOIAbstract
Pre-trained DNN model datasets for example codes of neural network pruning. Example pruning codes are published in "https://github.com/FujitsuLaboratories/CAC/tree/main/cac/pruning"
Pre-trained DNN model datasets for example codes of neural network pruning. Example pruning codes are published in "https://github.com/FujitsuLaboratories/CAC/tree/main/cac/pruning"
Network pruning is a promising avenue for compressing deep neural networks. A typical approach to pr...
Network pruning is a promising avenue for compressing deep neural networks. A typical approach to pr...
PyTorch 0.4 support An implementation of Baidu's RNN pruning paper from ICLR 2017 Narang, Sharan & ...
Pre-trained DNN model datasets for example codes of neural network pruning. Example pruning codes a...
The default multilayer neural network topology is a fully in-terlayer connected one. This simplistic...
In recent years, deep neural networks have achieved remarkable results in various artificial intelli...
Network pruning is an important research field aiming at reducing computational costs of neural netw...
This document is a work on pruning neural network for handwriting recognition. The aim of the work i...
Pruning connections in a fully connected neural network allows to remove redundancy in the structure...
Pruning connections in a fully connected neural network allows to remove redundancy in the structure...
International audienceThe training process of a neural network is the most time-consuming procedure ...
Gibbs pruning is a novel framework for expressing and designing neural network pruning methods. Comb...
The powerful performance of deep learning is evident to all. With the deepening of research, neural ...
Efficient model compression techniques are required to deploy deep neural networks (DNNs) on edge de...
This paper presents a survey of methods for pruning deep neural networks. It begins by categorising...
Network pruning is a promising avenue for compressing deep neural networks. A typical approach to pr...
Network pruning is a promising avenue for compressing deep neural networks. A typical approach to pr...
PyTorch 0.4 support An implementation of Baidu's RNN pruning paper from ICLR 2017 Narang, Sharan & ...
Pre-trained DNN model datasets for example codes of neural network pruning. Example pruning codes a...
The default multilayer neural network topology is a fully in-terlayer connected one. This simplistic...
In recent years, deep neural networks have achieved remarkable results in various artificial intelli...
Network pruning is an important research field aiming at reducing computational costs of neural netw...
This document is a work on pruning neural network for handwriting recognition. The aim of the work i...
Pruning connections in a fully connected neural network allows to remove redundancy in the structure...
Pruning connections in a fully connected neural network allows to remove redundancy in the structure...
International audienceThe training process of a neural network is the most time-consuming procedure ...
Gibbs pruning is a novel framework for expressing and designing neural network pruning methods. Comb...
The powerful performance of deep learning is evident to all. With the deepening of research, neural ...
Efficient model compression techniques are required to deploy deep neural networks (DNNs) on edge de...
This paper presents a survey of methods for pruning deep neural networks. It begins by categorising...
Network pruning is a promising avenue for compressing deep neural networks. A typical approach to pr...
Network pruning is a promising avenue for compressing deep neural networks. A typical approach to pr...
PyTorch 0.4 support An implementation of Baidu's RNN pruning paper from ICLR 2017 Narang, Sharan & ...
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