DeepCM: Deep neural networks to improve accuracy prediction of database cost models

2021 ◽  
Author(s):  
Abdelkader Ouared ◽  
Abdelhafid Chadli ◽  
Mohamed Amine Daoud
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Cost Models ◽  
Improve Accuracy

Comparing two different types of deep neural networks to improve accuracy in ECG intepretation

2021 ◽  
Author(s):  
Didi Liliana Popa ◽  
Mihai Lucian Mocanu ◽  
Radu-Teodoru Popa ◽  
Lucian-Florentin Barbulescu ◽  
Linda-Nicoleta Barbulescu
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Improve Accuracy ◽  
Different Types

scholarly journals Quantization of Deep Neural Networks for Accurate Edge Computing

2021 ◽  
Vol 17 (4) ◽  
pp. 1-11
Author(s):  
Wentao Chen ◽  
Hailong Qiu ◽  
Jian Zhuang ◽  
Chutong Zhang ◽  
Yu Hu ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Segmentation ◽  
Automatic Speech Recognition ◽  
Deep Neural Networks ◽  
Biomedical Image ◽  
Wide Range ◽  
Improve Accuracy ◽  
Memory Reduction ◽  
Fully Connected

Deep neural networks have demonstrated their great potential in recent years, exceeding the performance of human experts in a wide range of applications. Due to their large sizes, however, compression techniques such as weight quantization and pruning are usually applied before they can be accommodated on the edge. It is generally believed that quantization leads to performance degradation, and plenty of existing works have explored quantization strategies aiming at minimum accuracy loss. In this paper, we argue that quantization, which essentially imposes regularization on weight representations, can sometimes help to improve accuracy. We conduct comprehensive experiments on three widely used applications: fully connected network for biomedical image segmentation, convolutional neural network for image classification on ImageNet, and recurrent neural network for automatic speech recognition, and experimental results show that quantization can improve the accuracy by 1%, 1.95%, 4.23% on the three applications respectively with 3.5x-6.4x memory reduction.

Deep neural networks trained with heavier data augmentation learn features closer to representations in hIT

2018 ◽  
Author(s):  
Alex Hernández-García ◽  
Johannes Mehrer ◽  
Nikolaus Kriegeskorte ◽  
Peter König ◽  
Tim C. Kietzmann
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Data Augmentation

Representation of adversarial images in deep neural networks and the human brain

2018 ◽  
Author(s):  
Chi Zhang ◽  
Xiaohan Duan ◽  
Ruyuan Zhang ◽  
Li Tong
Keyword(s):  
Neural Networks ◽  
Human Brain ◽  
Deep Neural Networks

Semi-Orthogonal Low-Rank Matrix Factorization for Deep Neural Networks

2018 ◽  
Author(s):  
Daniel Povey ◽  
Gaofeng Cheng ◽  
Yiming Wang ◽  
Ke Li ◽  
Hainan Xu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Matrix Factorization ◽  
Deep Neural Networks ◽  
Low Rank ◽  
Rank Matrix ◽  
Low Rank Matrix
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