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Learning Defect Prediction from Unrealistic Data
To investigate this conjecture, we propose an approach for identifying the subsets of these large yet unrealistic datasets that are most similar to examples in real-world datasets based on their learned representations.
Robustness of Neural Networks: A Probabilistic and Practical Approach
Our algorithm uses abstract interpretation to approximate the behavior of a neural network and compute an overapproximation of the input regions that violate robustness.
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