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Found 4 papers, 2 papers with code
Imperceptible Adversarial Attacks on Tabular Data
Security of machine learning models is a concern as they may face adversarial attacks for unwarranted advantageous decisions.
How the Softmax Activation Hinders the Detection of Adversarial and Out-of-Distribution Examples in Neural Networks
Despite having excellent performances for a wide variety of tasks, modern neural networks are unable to provide a prediction with a reliable confidence estimate which would allow to detect misclassifications.
Concept Tree: High-Level Representation of Variables for More Interpretable Surrogate Decision Trees
Interpretable surrogates of black-box predictors trained on high-dimensional tabular datasets can struggle to generate comprehensible explanations in the presence of correlated variables.
Detecting Adversarial Examples and Other Misclassifications in Neural Networks by Introspection
Despite having excellent performances for a wide variety of tasks, modern neural networks are unable to provide a reliable confidence value allowing to detect misclassifications.
