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Found 3 papers, 2 papers with code
SoK: Explainable Machine Learning for Computer Security Applications
The security literature sometimes also fails to disentangle the role of the various stakeholders, e. g., by providing explanations to model users and designers while also exposing them to adversaries.
Learning state machines via efficient hashing of future traces
In this paper we propose a method to learn state machines from data streams using the count-min-sketch data structure to reduce memory requirements.
AMT 1.x: A toolbox for reproducible research in auditory modeling
The AMT aims for a consistent implementation of auditory models, well-structured in-code documentation, and inclusion of auditory data required to run the models.
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