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

26 July 2021

Paulina Stevia Nouwou Mindom

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

33 / 33 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
Operational range bounding of spectroscopy models with anomaly detection Luís F. Simoes Pierluigi Casale Marília Felismino K. H. Yip Ingo P. Waldmann Giovanna Tinetti T. Lueftinger 18 0 0 05 Aug 2024
Distributionally Robust Constrained Reinforcement Learning under Strong Duality Zhengfei Zhang Kishan Panaganti Laixi Shi Yanan Sui Adam Wierman Yisong Yue OOD 39 3 0 22 Jun 2024
Science based AI model certification for new operational environments with application in traffic state estimation Daryl Mupupuni Anupama Guntu Liang Hong Kamrul Hasan Leehyun Keel 21 0 0 13 May 2024
Forward Learning for Gradient-based Black-box Saliency Map Generation Zeliang Zhang Mingqian Feng Jinyang Jiang Rongyi Zhu Yijie Peng Chenliang Xu FAtt 34 2 0 22 Mar 2024
Testing Spintronics Implemented Monte Carlo Dropout-Based Bayesian Neural Networks Soyed Tuhin Ahmed Michael Hefenbrock G. Prenat L. Anghel M. Tahoori 24 0 0 09 Jan 2024
Learning Polynomial Representations of Physical Objects with Application to Certifying Correct Packing Configurations Morgan Jones 25 0 0 11 Dec 2023
METAL: Metamorphic Testing Framework for Analyzing Large-Language Model Qualities Sangwon Hyun Mingyu Guo Muhammad Ali Babar 36 8 0 11 Dec 2023
Modeling Risk in Reinforcement Learning: A Literature Mapping Leonardo Villalobos-Arias Derek Martin Abhijeet Krishnan Madeleine Gagné Colin M. Potts Arnav Jhala 20 0 0 08 Dec 2023
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 26 2 0 05 Dec 2023
GIST: Generated Inputs Sets Transferability in Deep Learning Florian Tambon Foutse Khomh G. Antoniol AAML 37 1 0 01 Nov 2023
SEE-OoD: Supervised Exploration For Enhanced Out-of-Distribution Detection Xiaoyang Song Wenbo Sun Maher Nouiehed Raed Al Kontar J. Jin OODD 34 0 0 12 Oct 2023
Data Cleaning and Machine Learning: A Systematic Literature Review Pierre-Olivier Coté Amin Nikanjam Nafisa Ahmed D. Humeniuk Foutse Khomh 44 22 0 03 Oct 2023
Verifying Safety of Neural Networks from Topological Perspectives Zhen Liang Dejin Ren Bai Xue J. Wang Wenjing Yang Wanwei Liu AAML 33 0 0 27 Jun 2023
Memory Efficient Neural Processes via Constant Memory Attention Block Leo Feng Frederick Tung Hossein Hajimirsadeghi Yoshua Bengio Mohamed Osama Ahmed 31 5 0 23 May 2023
Optimality Principles in Spacecraft Neural Guidance and Control Dario Izzo E. Blazquez Robin Ferede Sebastien Origer Christophe De Wagter Guido C. H. E de Croon 26 8 0 22 May 2023
Delay-SDE-net: A deep learning approach for time series modelling with memory and uncertainty estimates M. Eggen A. Midtfjord 21 2 0 14 Mar 2023
DeepGD: A Multi-Objective Black-Box Test Selection Approach for Deep Neural Networks Zohreh Aghababaeyan Manel Abdellatif Mahboubeh Dadkhah Lionel C. Briand AAML 34 16 0 08 Mar 2023
DiffTune: Auto-Tuning through Auto-Differentiation Sheng Cheng Minkyung Kim Lin Song Chengyu Yang Zhuohuan Wu Shenlong Wang N. Hovakimyan 36 6 0 20 Sep 2022
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 37 4 0 04 Sep 2022
A Probabilistic Framework for Mutation Testing in Deep Neural Networks Florian Tambon Foutse Khomh G. Antoniol MedIm 16 13 0 11 Aug 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 35 23 0 16 Apr 2022
Hardware Approximate Techniques for Deep Neural Network Accelerators: A Survey Giorgos Armeniakos Georgios Zervakis Dimitrios Soudris J. Henkel 217 93 0 16 Mar 2022
Safe AI -- How is this Possible? Harald Ruess Simon Burton 24 0 0 25 Jan 2022
Unsolved Problems in ML Safety Dan Hendrycks Nicholas Carlini John Schulman Jacob Steinhardt 186 275 0 28 Sep 2021
Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning Lukas Brunke Melissa Greeff Adam W. Hall Zhaocong Yuan Siqi Zhou Jacopo Panerati Angela P. Schoellig OffRL 34 603 0 13 Aug 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 67 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 60 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 1,234 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 47 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 249 1,838 0 03 Feb 2017
Safety Verification of Deep Neural Networks Xiaowei Huang Marta Kwiatkowska Sen Wang Min Wu AAML 180 932 0 21 Oct 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,145 0 06 Jun 2015