5 Result(s)

Article

Global optimization of objective functions represented by ReLU networks

Neural networks can learn complex, non-convex functions, and it is challenging to guarantee their correct behavior in safety-critical contexts. Many approaches exist to find failures in networks (e.g., adversa...

Christopher A. Strong, Haoze Wu, Aleksandar Zeljić, Kyle D. Julian… in Machine Learning (2023)

Article

Guest Editorial: Special issue on robust machine learning

Ransalu Senanayake, Daniel J. Fremont, Mykel J. Kochenderfer… in Machine Learning (2023)

Article

Generating probabilistic safety guarantees for neural network controllers

Neural networks serve as effective controllers in a variety of complex settings due to their ability to represent expressive policies. The complex nature of neural networks, however, makes their output difficu...

Sydney M. Katz, Kyle D. Julian, Christopher A. Strong… in Machine Learning (2023)

Chapter and Conference Paper

The Marabou Framework for Verification and Analysis of Deep Neural Networks

Deep neural networks are revolutionizing the way complex systems are designed. Consequently, there is a pressing need for tools and techniques for network analysis and certification. To help in addressing that...

Guy Katz, Derek A. Huang, Duligur Ibeling, Kyle Julian… in Computer Aided Verification (2019)

Chapter and Conference Paper

Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks

Deep neural networks have emerged as a widely used and effective means for tackling complex, real-world problems. However, a major obstacle in applying them to safety-critical systems is the great difficulty i...

Guy Katz, Clark Barrett, David L. Dill, Kyle Julian… in Computer Aided Verification (2017)