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Found 5 papers, 1 papers with code
Reinforcement Learning and Graph Neural Networks for Probabilistic Risk Assessment
This paper presents a new approach to the solution of Probabilistic Risk Assessment (PRA) models using the combination of Reinforcement Learning (RL) and Graph Neural Networks (GNNs).
Investigating the Corruption Robustness of Image Classifiers with Random Lp-norm Corruptions
In the field of adversarial robustness of image classifiers, robustness is commonly defined as the stability of a model to all input changes within a p-norm distance.
Small Spacecraft for Global Greenhouse Gas Monitoring
This work is devoted to the capabilities analysis of constellation and small spacecraft developed using CubeSat technology to solve promising problems of the Earth remote sensing in the area of greenhouse gases emissions.
Utilizing Class Separation Distance for the Evaluation of Corruption Robustness of Machine Learning Classifiers
Furthermore, we observe unexpected optima in classifiers robust accuracy through training and testing classifiers with different levels of noise.
Fault Injectors for TensorFlow: Evaluation of the Impact of Random Hardware Faults on Deep CNNs
The results demonstrate how random bit flips in the output of particular mathematical operations and layers of NNs affect the classification accuracy.
