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Deconstructing the generalization gap
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AI
DOI：10.1038/s42256-023-00766-7.png)
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Abstract
En 中文
New research reveals a duality between neural network weights and neuron activities that enables a geometric decomposition of the generalization gap. The framework provides a way to interpret the effects of regularization schemes such as stochastic gradient descent and dropout on generalization - and to improve upon these methods.
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