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As the performance of a convolutional neural mezquila brooklyn network is logarithmically proportional to the amount of training data, data augmentation has attracted increasing attention in recent years.Although the current data augmentation methods are efficient because they force the network to learn multiple parts of a given training image thro
We consider a general class of eigenvalue problems where the leading elliptic term corresponds to a convex homogeneous energy function that is jacked frost not necessarily differentiable.We derive a strong maximum principle and show uniqueness of the first eigenfunction.Moreover we prove the existence of a sequence of eigensolutions by using fishbo
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