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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Title: Establishing Trust in Neural Networks with Representation Shifts
Committee:
Dr. AlRegib, Advisor
Dr. Dyer, Chair
Dr. Kira
Abstract: The objective of the proposed research is to improve the trust in neural networks with second-order representation shifts. While neural networks are effective in modeling complex data dependencies, human trust remains a major obstacle for industrial deployment and network predictions are frequently met with sincere skepticism. In this work, I propose establishing trust by integrating robustness, consistency, and uncertainty-awareness natively within the neural network paradigm. For this purpose, I extract statistics from shifts of neural network representations (second-order representations) and predict the learning difficulty during deployment. The approach is modular and generalizable to applications where deterministic neural networks are deployed.
