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A Neurosymbolic Framework for Bias Correction in CNNs
We demonstrate that our NeSyBiCor framework successfully corrects the biases of CNNs trained with subsets of classes from the Places dataset while sacrificing minimal accuracy and improving interpretability, by greatly decreasing the size of the final bias-corrected rule-set w. r. t.
Logic of Differentiable Logics: Towards a Uniform Semantics of DL
A DL consists of a syntax in which specifications are stated and an interpretation function that translates expressions in the syntax into loss functions.
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