A linear constraint driven approach to efficiently enhancing branch and bound in neural network verification
Chavez, Jorge
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https://hdl.handle.net/2142/129353
Description
Title
A linear constraint driven approach to efficiently enhancing branch and bound in neural network verification
Author(s)
Chavez, Jorge
Issue Date
2025-05-09
Director of Research (if dissertation) or Advisor (if thesis)
Zhang, Huan
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Machine Learning
Neural Network Verification
Language
eng
Abstract
The verification of neural network systems is crucial as the adoption of these systems are considered for safety-critical tasks. A neural network system that works empirically well may not be robust, and when employed in areas such as cyber-security and cyber-physical systems, the guaranteed performance is a must. Formal verification is a rapidly growing field that delves into providing these guarantees, ensuring that properties on these networks can be assured. This thesis serves as an introduction into the common techniques used to provide such guarantees. There is a particular focus on bound propagation techniques as such techniques have fueled state-of-the-art, efficient verifiers. After covering the many advances that have been made in neural network verification, we will delve further into the branch-and-bound paradigm that typically accompanies many existing verifiers, as well as demonstrate an insightful algorithm that is capable of garnering further efficacy from bound propagation verifiers.
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