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• W3093554712 abstract "Invariances to translations have imbued convolutional neural networks with powerful generalization properties. However, we often do not know a priori what invariances are present in the data, or to what extent a model should be invariant to a given symmetry group. We show how to emph{learn} invariances and equivariances by parameterizing a distribution over augmentations and optimizing the training loss simultaneously with respect to the network parameters and augmentation parameters. With this simple procedure we can recover the correct set and extent of invariances on image classification, regression, segmentation, and molecular property prediction from a large space of augmentations, on training data alone." @default.
• W3093554712 created "2020-10-29" @default.
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• W3093554712 date "2020-10-22" @default.
• W3093554712 modified "2023-09-27" @default.
• W3093554712 title "Learning Invariances in Neural Networks" @default.
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• W3093554712 hasPublicationYear "2020" @default.
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