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How to Certify Machine Learning Based Safety-critical Systems? A Systematic Literature Review
26 July 2021
Florian Tambon
Gabriel Laberge
Le An
Amin Nikanjam
Paulina Stevia Nouwou Mindom
Y. Pequignot
Foutse Khomh
G. Antoniol
E. Merlo
François Laviolette
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Papers citing
"How to Certify Machine Learning Based Safety-critical Systems? A Systematic Literature Review"
28 / 28 papers shown
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Learning Polynomial Representations of Physical Objects with Application to Certifying Correct Packing Configurations
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Modeling Risk in Reinforcement Learning: A Literature Mapping
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Verifying Safety of Neural Networks from Topological Perspectives
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Memory Efficient Neural Processes via Constant Memory Attention Block
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Optimality Principles in Spacecraft Neural Guidance and Control
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Delay-SDE-net: A deep learning approach for time series modelling with memory and uncertainty estimates
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DeepGD: A Multi-Objective Black-Box Test Selection Approach for Deep Neural Networks
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DiffTune: Auto-Tuning through Auto-Differentiation
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Perception Simplex: Verifiable Collision Avoidance in Autonomous Vehicles Amidst Obstacle Detection Faults
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Ergo, SMIRK is Safe: A Safety Case for a Machine Learning Component in a Pedestrian Automatic Emergency Brake System
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Hardware Approximate Techniques for Deep Neural Network Accelerators: A Survey
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Safe AI -- How is this Possible?
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Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning
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