Information extraction from low-quality or questionable sources

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Authors
Wang, Ren
Issue Date
2020-08
Type
Electronic thesis
Thesis
Language
ENG
Keywords
Electrical engineering
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Abstract
With the growth of the amount of data and the extensive application of deep learning models, low-quality data and low-security models become problems for industry and users. For example, some data are noisy, corrupted, partially observed, and maybe even highly quantized. Real information is hidden in these low-quality data and difficult to be mined. Even more disturbingly, models could be poisoned by attackers with some well-designed methods and data. Users will make decisions based on these low-security models. Hence, extracting accurate information from questionable sources is extremely important. We will address the problem from two perspectives: (1) Estimate the ground truth data and the clusters from low-quality measurements (2) Extract the information of attack patterns from model parameters.
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August 2020
School of Engineering
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Rensselaer Polytechnic Institute, Troy, NY
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