How to Certify Machine Learning Based Safety-critical Systems? A
Systematic Literature Review

How to Certify Machine Learning Based Safety-critical Systems? A Systematic Literature Review

26 July 2021

Gabriel Laberge

Paulina Stevia Nouwou Mindom

François Laviolette

Papers citing "How to Certify Machine Learning Based Safety-critical Systems? A Systematic Literature Review"

15 / 15 papers shown

Title
CoCoAFusE: Beyond Mixtures of Experts via Model Fusion Aurelio Raffa Ugolini M. Tanelli Valentina Breschi MoE 24 0 0 02 May 2025
Synergistic Perception and Control Simplex for Verifiable Safe Vertical Landing Ayoosh Bansal Yang Zhao James Zhu Sheng Cheng Yuliang Gu Hyung-Jin Yoon Hunmin Kim N. Hovakimyan Lui Sha 13 2 0 05 Dec 2023
Memory Efficient Neural Processes via Constant Memory Attention Block Leo Feng Frederick Tung Hossein Hajimirsadeghi Yoshua Bengio Mohamed Osama Ahmed 25 5 0 23 May 2023
Optimality Principles in Spacecraft Neural Guidance and Control Dario Izzo E. Blazquez Robin Ferede Sebastien Origer C. De Wagter Guido de Croon 24 8 0 22 May 2023
DeepGD: A Multi-Objective Black-Box Test Selection Approach for Deep Neural Networks Zohreh Aghababaeyan Manel Abdellatif Mahboubeh Dadkhah Lionel C. Briand AAML 28 15 0 08 Mar 2023
Perception Simplex: Verifiable Collision Avoidance in Autonomous Vehicles Amidst Obstacle Detection Faults Ayoosh Bansal Hunmin Kim Simon Yu Bo-wen Li N. Hovakimyan Marco Caccamo L. Sha AAML 26 4 0 04 Sep 2022
Ergo, SMIRK is Safe: A Safety Case for a Machine Learning Component in a Pedestrian Automatic Emergency Brake System Markus Borg Jens Henriksson Kasper Socha Olof Lennartsson Elias Sonnsjo Lonegren T. Bui Piotr Tomaszewski S. Sathyamoorthy Sebastian Brink M. H. Moghadam 22 23 0 16 Apr 2022
Unsolved Problems in ML Safety Dan Hendrycks Nicholas Carlini John Schulman Jacob Steinhardt 186 273 0 28 Sep 2021
Learn2Perturb: an End-to-end Feature Perturbation Learning to Improve Adversarial Robustness Ahmadreza Jeddi M. Shafiee Michelle Karg C. Scharfenberger A. Wong OOD AAML 50 63 0 02 Mar 2020
Analyzing the Noise Robustness of Deep Neural Networks Kelei Cao Mengchen Liu Hang Su Jing Wu Jun Zhu Shixia Liu AAML 52 89 0 26 Jan 2020
An Introduction to Deep Reinforcement Learning Vincent François-Lavet Peter Henderson Riashat Islam Marc G. Bellemare Joelle Pineau OffRL AI4CE 80 1,231 0 30 Nov 2018
DeepSafe: A Data-driven Approach for Checking Adversarial Robustness in Neural Networks D. Gopinath Guy Katz C. Păsăreanu Clark W. Barrett AAML 42 87 0 02 Oct 2017
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks Guy Katz Clark W. Barrett D. Dill Kyle D. Julian Mykel Kochenderfer AAML 228 1,835 0 03 Feb 2017
Safety Verification of Deep Neural Networks Xiaowei Huang M. Kwiatkowska Sen Wang Min Wu AAML 178 932 0 21 Oct 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,136 0 06 Jun 2015