Adversarial Attack for Uncertainty Estimation: Identifying Critical Regions in Neural Networks
Authors: Alarab, I. and Prakoonwit, S.
Journal: Neural Processing Letters
Volume: 54
Issue: 3
Pages: 1805-1821
eISSN: 1573-773X
ISSN: 1370-4621
DOI: 10.1007/s11063-021-10707-3
Abstract:We propose a novel method to capture data points near decision boundary in neural network that are often referred to a specific type of uncertainty. In our approach, we sought to perform uncertainty estimation based on the idea of adversarial attack method. In this paper, uncertainty estimates are derived from the input perturbations, unlike previous studies that provide perturbations on the model's parameters as in Bayesian approach. We are able to produce uncertainty with couple of perturbations on the inputs. Interestingly, we apply the proposed method to datasets derived from blockchain. We compare the performance of model uncertainty with the most recent uncertainty methods. We show that the proposed method has revealed a significant outperformance over other methods and provided less risk to capture model uncertainty in machine learning.
https://eprints.bournemouth.ac.uk/36455/
Source: Scopus
Adversarial Attack for Uncertainty Estimation: Identifying Critical Regions in Neural Networks
Authors: Alarab, I. and Prakoonwit, S.
Journal: NEURAL PROCESSING LETTERS
Volume: 54
Issue: 3
Pages: 1805-1821
eISSN: 1573-773X
ISSN: 1370-4621
DOI: 10.1007/s11063-021-10707-3
https://eprints.bournemouth.ac.uk/36455/
Source: Web of Science (Lite)
Adversarial Attack for Uncertainty Estimation: Identifying Critical Regions in Neural Networks
Authors: Alarab, I. and Prakoonwit, S.
Journal: Neural Processing Letters
eISSN: 1573-773X
ISSN: 1370-4621
DOI: 10.1007/s11063-021-10707-3
Abstract:We propose a novel method to capture data points near decision boundary in neural network that are often referred to a specific type of uncertainty. In our approach, we sought to perform uncertainty estimation based on the idea of adversarial attack method. In this paper, uncertainty estimates are derived from the input perturbations, unlike previous studies that provide perturbations on the model's parameters as in Bayesian approach. We are able to produce uncertainty with couple of perturbations on the inputs. Interestingly, we apply the proposed method to datasets derived from blockchain. We compare the performance of model uncertainty with the most recent uncertainty methods. We show that the proposed method has revealed a significant outperformance over other methods and provided less risk to capture model uncertainty in machine learning.
https://eprints.bournemouth.ac.uk/36455/
Source: Manual
Preferred by: Simant Prakoonwit
Adversarial Attack for Uncertainty Estimation: Identifying Critical Regions in Neural Networks
Authors: Alarab, I. and Prakoonwit, S.
Abstract:We propose a novel method to capture data points near decision boundary in neural network that are often referred to a specific type of uncertainty. In our approach, we sought to perform uncertainty estimation based on the idea of adversarial attack method. In this paper, uncertainty estimates are derived from the input perturbations, unlike previous studies that provide perturbations on the model's parameters as in Bayesian approach. We are able to produce uncertainty with couple of perturbations on the inputs. Interestingly, we apply the proposed method to datasets derived from blockchain. We compare the performance of model uncertainty with the most recent uncertainty methods.
We show that the proposed method has revealed a significant outperformance over other methods and provided less risk to capture model uncertainty in machine learning.
https://eprints.bournemouth.ac.uk/36455/
http://dx.doi.org/10.1007/s11063-021-10707-3
Source: arXiv
Adversarial Attack for Uncertainty Estimation: Identifying Critical Regions in Neural Networks
Authors: Alarab, I. and Prakoonwit, S.
Journal: Neural Processing Letters
Volume: 54
Pages: 1805-1821
ISSN: 1370-4621
Abstract:We propose a novel method to capture data points near decision boundary in neural network that are often referred to a specific type of uncertainty. In our approach, we sought to perform uncertainty estimation based on the idea of adversarial attack method. In this paper, uncertainty estimates are derived from the input perturbations, unlike previous studies that provide perturbations on the model’s parameters as in Bayesian approach. We are able to produce uncertainty with couple of perturbations on the inputs. Interestingly, we apply the proposed method to datasets derived from blockchain. We compare the performance of model uncertainty with the most recent uncertainty methods. We show that the proposed method has revealed a significant outperformance over other methods and provided less risk to capture model uncertainty in machine learning
https://eprints.bournemouth.ac.uk/36455/
Source: BURO EPrints