Information extraction from low-quality or questionable sources

Authors
Wang, Ren
ORCID
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Other Contributors
Wang, Meng
Chow, J. H. (Joe H.), 1951-
Radke, Richard J., 1974-
Xu, Yangyang
Issue Date
2020-08
Keywords
Electrical engineering
Degree
PhD
Terms of Use
This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
Full Citation
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.
Description
August 2020
School of Engineering
Department
Dept. of Electrical, Computer, and Systems Engineering
Publisher
Rensselaer Polytechnic Institute, Troy, NY
Relationships
Rensselaer Theses and Dissertations Online Collection
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