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Found 2 papers, 1 papers with code
Towards Inferential Reproducibility of Machine Learning Research
Instead of removing noise, we propose to incorporate several sources of variance, including their interaction with data properties, into an analysis of significance and reliability of machine learning evaluation, with the aim to draw inferences beyond particular instances of trained models.
SMARAGD: Learning SMatch for Accurate and Rapid Approximate Graph Distance
The similarity of graph structures, such as Meaning Representations (MRs), is often assessed via structural matching algorithms, such as Smatch (Cai and Knight, 2013).
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