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In machine learning, normalization is a statistical technique with various applications. There are two main forms of normalization, namely data normalization and activation normalization . Data normalization (or feature scaling ) includes methods that rescale input data so that the features have the same range, mean, variance, or other ...
An abstract rewriting system is strongly normalizing, terminating, noetherian, or has the (strong) normalization property (SN), if each of its objects is strongly normalizing. [ 2 ] A rewriting system has the normal form property (NF) if for all objects a and normal forms b , b can be reached from a by a series of rewrites and inverse rewrites ...
And if the datatype of normal forms is typed, the type of reify (and therefore of nbe) then makes it clear that normalization is type preserving. [ 9 ] Normalization by evaluation also scales to the simply typed lambda calculus with sums ( + ), [ 7 ] using the delimited control operators shift and reset .
Normalization model, used in visual neuroscience; Normalization in quantum mechanics, see Wave function § Normalization condition and normalized solution; Normalization (sociology) or social normalization, the process through which ideas and behaviors that may fall outside of social norms come to be regarded as "normal"
Batch normalization (also known as batch norm) is a method used to make training of artificial neural networks faster and more stable through normalization of the layers' inputs by re-centering and re-scaling. It was proposed by Sergey Ioffe and Christian Szegedy in 2015.
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Inception v2 was released in 2015, in a paper that is more famous for proposing batch normalization. [7] [8] It had 13.6 million parameters.It improves on Inception v1 by adding batch normalization, and removing dropout and local response normalization which they found became unnecessary when batch normalization is used.
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