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Model multiplicity is a well-known but poorly understood phenomenon that undermines the generalisation guarantees of machine learning models.
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On large-batch training for deep learning: Generalization gap and sharp minima
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All models are wrong, but many are useful: Learning a variable’s importance by studying an entire class of prediction models simultaneously
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Semenova, L., Rudin, C., and Parr, R · 2019
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Averaging weights leads to wider optima and better generalization
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Chen, Z., Wang, Y., Lin, D., Cheng, D., Hong, L., Chi, E., and Cui, C · 2020
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Underspecification presents challenges for credibility in modern machine learning
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Smooth activations and reproducibility in deep networks
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Bayesian deep learning and a probabilistic perspective of generalization
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