Global Optimization of Objective Functions Represented by ReLU Networks

Global Optimization of Objective Functions Represented by ReLU Networks

7 October 2020

Christopher A. Strong

Aleksandar Zeljić

Clark W. Barrett

Mykel J. Kochenderfer

Papers citing "Global Optimization of Objective Functions Represented by ReLU Networks"

7 / 7 papers shown

Title
When Deep Learning Meets Polyhedral Theory: A Survey Joey Huchette Gonzalo Muñoz Thiago Serra Calvin Tsay AI4CE 91 32 0 29 Apr 2023
Constrained Reinforcement Learning for Robotics via Scenario-Based Programming Davide Corsi Raz Yerushalmi Guy Amir Alessandro Farinelli D. Harel Guy Katz 14 19 0 20 Jun 2022
Verifying Learning-Based Robotic Navigation Systems Guy Amir Davide Corsi Raz Yerushalmi Luca Marzari D. Harel Alessandro Farinelli Guy Katz 74 36 0 26 May 2022
An Abstraction-Refinement Approach to Verifying Convolutional Neural Networks Matan Ostrovsky Clark W. Barrett Guy Katz 21 26 0 06 Jan 2022
CNN-Cert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networks Akhilan Boopathy Tsui-Wei Weng Pin-Yu Chen Sijia Liu Luca Daniel AAML 101 138 0 29 Nov 2018
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks Guy Katz Clark W. Barrett D. Dill Kyle D. Julian Mykel Kochenderfer AAML 222 1,818 0 03 Feb 2017
Safety Verification of Deep Neural Networks Xiaowei Huang M. Kwiatkowska Sen Wang Min Wu AAML 178 929 0 21 Oct 2016