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Estimating the Generalization Error (GE) of Deep Neural Networks (DNNs) is an important task that often relies on availability of held-out data.
An almost unbiased method of obtaining confidence intervals for the probability of misclassification in discriminant analysis
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Understanding deep learning requires rethinking generalization
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Wide neural networks of any depth evolve as linear models under gradient descent
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Robustness May Be at Odds with Accuracy
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Non-vacuous Generalization Bounds at the ImageNet Scale: A PAC-Bayesian Compression Approach
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Information in infinite ensembles of infinitely-wide neural networks
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Methods and Analysis of The First Competition in Predicting Generalization of Deep Learning
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Understanding deep learning (still) requires rethinking generalization
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Information Flow in Deep Neural Networks
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