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  2. Variational autoencoder - Wikipedia

    en.wikipedia.org/wiki/Variational_autoencoder

    In machine learning, a variational autoencoder (VAE) is an artificial neural network architecture introduced by Diederik P. Kingma and Max Welling. [1] It is part of the families of probabilistic graphical models and variational Bayesian methods .

  3. Autoencoder - Wikipedia

    en.wikipedia.org/wiki/Autoencoder

    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.

  4. Reparameterization trick - Wikipedia

    en.wikipedia.org/wiki/Reparameterization_trick

    More generally, other distributions can be used than the Bernoulli distribution, such as the gaussian noise: = +, (,) where = and =, with and being the mean and variance of the -th output neuron. The reparameterization trick can be applied to all such cases, resulting in the variational dropout method.

  5. Variance - Wikipedia

    en.wikipedia.org/wiki/Variance

    In probability theory and statistics, variance is the expected value of the squared deviation from the mean of a random variable. The standard deviation (SD) ...

  6. Bias–variance tradeoff - Wikipedia

    en.wikipedia.org/wiki/Bias–variance_tradeoff

    In k-nearest neighbor models, a high value of k leads to high bias and low variance (see below). In instance-based learning, regularization can be achieved varying the mixture of prototypes and exemplars. [13] In decision trees, the depth of the tree determines the variance. Decision trees are commonly pruned to control variance. [7]: 307

  7. Autoregressive model - Wikipedia

    en.wikipedia.org/wiki/Autoregressive_model

    In statistics, econometrics, and signal processing, an autoregressive (AR) model is a representation of a type of random process; as such, it can be used to describe certain time-varying processes in nature, economics, behavior, etc.

  8. Algorithms for calculating variance - Wikipedia

    en.wikipedia.org/wiki/Algorithms_for_calculating...

    Algorithms for calculating variance play a major role in computational statistics.A key difficulty in the design of good algorithms for this problem is that formulas for the variance may involve sums of squares, which can lead to numerical instability as well as to arithmetic overflow when dealing with large values.

  9. Graphical model - Wikipedia

    en.wikipedia.org/wiki/Graphical_model

    Generally, probabilistic graphical models use a graph-based representation as the foundation for encoding a distribution over a multi-dimensional space and a graph that is a compact or factorized representation of a set of independences that hold in the specific distribution.