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The reparameterization trick (aka "reparameterization gradient estimator") is a technique used in statistical machine learning, particularly in variational inference, variational autoencoders, and stochastic optimization.
The scheme of the reparameterization trick. The randomness variable is injected into the latent space as external input. In this way, it is possible to backpropagate the gradient without involving stochastic variable during the update.
Derivation by reparameterization. ... The encoder-decoder pair is most often a variational autoencoder (VAE). Architectural improvements
In machine learning, normalization is a statistical technique with various applications. There are two main forms of normalization, namely data normalization and activation normalization.
A flow-based generative model is a generative model used in machine learning that explicitly models a probability distribution by leveraging normalizing flow, [1] [2] [3] which is a statistical method using the change-of-variable law of probabilities to transform a simple distribution into a complex one.
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An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning).An autoencoder learns two functions: an encoding function that transforms the input data, and a decoding function that recreates the input data from the encoded representation.
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