Interval universal approximation for neural networks

Abstract

To verify safety and robustness of neural networks, researchers have successfully applied abstract interpretation , primarily using the interval abstract domain. In this paper, we study the theoretical power and limits of the interval domain for neural-network verification.

Document Details

Document Type
Pub Defense Publication
Publication Date
Jan 12, 2022
Source ID
10.1145/3498675

Entities

People

  • Aws Albarghouthi
  • Gautam Prakriya
  • Somesh Jha
  • Zi Wang

Organizations

  • Air Force Research Laboratory
  • Army Research Office
  • National Science Foundation
  • The Chinese University of Hong Kong
  • University of Wisconsin–Madison

Tags

Fields of Study

  • Computer science

Readers

  • Calculus or Mathematical Analysis
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks