Shale Anisotropy Model Building Based on Deep Neural Networks

2020 ◽  
Vol 125 (2) ◽  
Author(s):  
Nan You ◽  
Yunyue Elita Li ◽  
Arthur Cheng
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Model Building ◽  
Anisotropy Model ◽  
Shale Anisotropy

scholarly journals Sensitivity Analysis of Deep Neural Networks

2019 ◽  
Vol 33 ◽  
pp. 4943-4950 ◽  
Author(s):  
Hai Shu ◽  
Hongtu Zhu
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Model Building ◽  
Information Geometry ◽  
Superior Performance ◽  
Invariance Properties ◽  
Influence Measure ◽  
Network Sensitivity ◽  
Adversarial Examples ◽  
Test Sets

Deep neural networks (DNNs) have achieved superior performance in various prediction tasks, but can be very vulnerable to adversarial examples or perturbations. Therefore, it is crucial to measure the sensitivity of DNNs to various forms of perturbations in real applications. We introduce a novel perturbation manifold and its associated influence measure to quantify the effects of various perturbations on DNN classifiers. Such perturbations include various external and internal perturbations to input samples and network parameters. The proposed measure is motivated by information geometry and provides desirable invariance properties. We demonstrate that our influence measure is useful for four model building tasks: detecting potential ‘outliers’, analyzing the sensitivity of model architectures, comparing network sensitivity between training and test sets, and locating vulnerable areas. Experiments show reasonably good performance of the proposed measure for the popular DNN models ResNet50 and DenseNet121 on CIFAR10 and MNIST datasets.

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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