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Found 2 papers, 1 papers with code
Dangers of Bayesian Model Averaging under Covariate Shift
Approximate Bayesian inference for neural networks is considered a robust alternative to standard training, often providing good performance on out-of-distribution data.
Automated assessment of knowledge hierarchy evolution: comparing directed acyclic graphs
In this paper, we focus on measures that leverage structural properties of the knowledge hierarchy graph to assess the temporal changes.
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