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Found 2 papers, 2 papers with code
Provable concept learning for interpretable predictions using variational autoencoders
In safety-critical applications, practitioners are reluctant to trust neural networks when no interpretable explanations are available.
Fast Rates for Noisy Interpolation Require Rethinking the Effects of Inductive Bias
Good generalization performance on high-dimensional data crucially hinges on a simple structure of the ground truth and a corresponding strong inductive bias of the estimator.
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