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The explanation made in the original paper [1] was that batch norm works by reducing internal covariate shift, but this has been challenged by more recent work. One experiment [2] trained a VGG-16 network [5] under 3 different training regimes: standard (no batch norm), batch norm, and batch norm with noise added to each layer during training ...
In machine learning, a hyperparameter is a parameter that can be set in order to define any configurable part of a model's learning process. Hyperparameters can be classified as either model hyperparameters (such as the topology and size of a neural network) or algorithm hyperparameters (such as the learning rate and the batch size of an optimizer).
In machine learning, hyperparameter optimization [1] or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process, which must be configured before the process starts. [2] [3]
In mathematics, statistics, finance, [1] and computer science, particularly in machine learning and inverse problems, regularization is a process that converts the answer of a problem to a simpler one. It is often used in solving ill-posed problems or to prevent overfitting. [2]
The achievable H ∞ norm of the closed loop system is mainly given through the matrix D 11 (when the system P is given in the form (A, B 1, B 2, C 1, C 2, D 11, D 12, D 22, D 21)). There are several ways to come to an H ∞ controller: A Youla-Kucera parametrization of the closed loop often leads to very high-order controller.
In general, instead of e a different base b > 0 can be used. As above, if b > 1 then larger input components will result in larger output probabilities, and increasing the value of b will create probability distributions that are more concentrated around the positions of the largest input values.
The Ziegler–Nichols tuning (represented by the 'Classic PID' equations in the table above) creates a "quarter wave decay". This is an acceptable result for some purposes, but not optimal for all applications. This tuning rule is meant to give PID loops best disturbance rejection. [2]
In many cases, this matrix is chosen as a scalar multiple of the identity matrix (=), giving preference to solutions with smaller norms; this is known as L 2 regularization. [20] In other cases, high-pass operators (e.g., a difference operator or a weighted Fourier operator ) may be used to enforce smoothness if the underlying vector is ...