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  2. Normalization (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Normalization_(machine...

    Weight normalization (WeightNorm) [18] is a technique inspired by BatchNorm that normalizes weight matrices in a neural network, rather than its activations. One example is spectral normalization , which divides weight matrices by their spectral norm .

  3. Batch normalization - Wikipedia

    en.wikipedia.org/wiki/Batch_normalization

    In a neural network, batch normalization is achieved through a normalization step that fixes the means and variances of each layer's inputs. Ideally, the normalization would be conducted over the entire training set, but to use this step jointly with stochastic optimization methods, it is impractical to use the global information. Thus ...

  4. Flow-based generative model - Wikipedia

    en.wikipedia.org/wiki/Flow-based_generative_model

    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.

  5. Nondimensionalization - Wikipedia

    en.wikipedia.org/wiki/Nondimensionalization

    One important use is in the analysis of control systems. One of the simplest characteristic units is the doubling time of a system experiencing exponential growth , or conversely the half-life of a system experiencing exponential decay ; a more natural pair of characteristic units is mean age/ mean lifetime , which correspond to base e rather ...

  6. Vanishing gradient problem - Wikipedia

    en.wikipedia.org/wiki/Vanishing_gradient_problem

    Weight initialization [ edit ] Kumar suggested that the distribution of initial weights should vary according to activation function used and proposed to initialize the weights in networks with the logistic activation function using a Gaussian distribution with a zero mean and a standard deviation of 3.6/sqrt(N) , where N is the number of ...

  7. Feature scaling - Wikipedia

    en.wikipedia.org/wiki/Feature_scaling

    Without normalization, the clusters were arranged along the x-axis, since it is the axis with most of variation. After normalization, the clusters are recovered as expected. In machine learning, we can handle various types of data, e.g. audio signals and pixel values for image data, and this data can include multiple dimensions. Feature ...

  8. These convicted felons say if Trump can be elected president ...

    www.aol.com/convicted-felons-trump-elected...

    CNN asked convicted felons about their struggles, their hopes and how they feel about President-elect Donald Trump, who will return to the White House after being convicted of 34 felony charges.

  9. Flow cytometry bioinformatics - Wikipedia

    en.wikipedia.org/wiki/Flow_cytometry_bioinformatics

    Normalization methods to remove technical variance, frequently derived from image registration techniques, are thus a critical step in many flow cytometry analyses. Single-marker normalization can be performed using landmark registration, in which peaks in a kernel density estimate of each sample are identified and aligned across samples. [24]

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