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  2. Softplus - Wikipedia

    en.wikipedia.org/wiki/Softplus

    The convex conjugate (specifically, the Legendre transform) of the softplus function is the negative binary entropy (with base e).This is because (following the definition of the Legendre transform: the derivatives are inverse functions) the derivative of softplus is the logistic function, whose inverse function is the logit, which is the derivative of negative binary entropy.

  3. Activation function - Wikipedia

    en.wikipedia.org/wiki/Activation_function

    The activation function of a node in an artificial neural network is a function that calculates the output of the node based on its individual inputs and their weights. Nontrivial problems can be solved using only a few nodes if the activation function is nonlinear .

  4. Rectifier (neural networks) - Wikipedia

    en.wikipedia.org/wiki/Rectifier_(neural_networks)

    Plot of the ReLU (blue) and GELU (green) functions near x = 0. In the context of artificial neural networks, the rectifier or ReLU (rectified linear unit) activation function [1] [2] is an activation function defined as the non-negative part of its argument, i.e., the ramp function:

  5. Swish function - Wikipedia

    en.wikipedia.org/wiki/Swish_function

    The swish function is a family of mathematical function defined as follows: . The swish function ⁡ = ⁡ = +. [1]. where can be constant (usually set to 1) or trainable.. The swish family was designed to smoothly interpolate between a linear function and the ReLU function.

  6. Ramp function - Wikipedia

    en.wikipedia.org/wiki/Ramp_function

    In mathematics, the ramp function is also known as the positive part. In machine learning, it is commonly known as a ReLU activation function [1] [2] or a rectifier in analogy to half-wave rectification in electrical engineering. In statistics (when used as a likelihood function) it is known as a tobit model.

  7. Universal approximation theorem - Wikipedia

    en.wikipedia.org/wiki/Universal_approximation...

    Also, certain non-continuous activation functions can be used to approximate a sigmoid function, which then allows the above theorem to apply to those functions. For example, the step function works. In particular, this shows that a perceptron network with a single infinitely wide hidden layer can approximate arbitrary functions.

  8. RLU - Wikipedia

    en.wikipedia.org/wiki/RLU

    Rectified linear unit, a neuron activation function used in neural networks, usually referred to as an ReLU; Relative light unit, a unit for measuring cleanliness by measuring the levels of Adenosine Triphosphate; Remote line unit, a type of switch in the GTD-5 EAX switching system; RLU-1 Breezy, an American homebuilt aircraft design

  9. Reproducing kernel Hilbert space - Wikipedia

    en.wikipedia.org/wiki/Reproducing_kernel_Hilbert...

    The ReLU function is commonly defined as () = {,} and is a mainstay in the architecture of neural networks where it is used as an activation function. One can construct a ReLU-like nonlinear function using the theory of reproducing kernel Hilbert spaces.