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Found 2 papers, 2 papers with code
Exploring the Trade-off Between Model Performance and Explanation Plausibility of Text Classifiers Using Human Rationales
By leveraging a multi-objective optimization algorithm, we explore the trade-off between the two loss functions and generate a Pareto-optimal frontier of models that balance performance and plausibility.
Distill n' Explain: explaining graph neural networks using simple surrogates
We also propose FastDnX, a faster version of DnX that leverages the linear decomposition of our surrogate model.
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