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Found 7 papers, 1 papers with code
Smooth Maximum Unit: Smooth Activation Function for Deep Networks Using Smoothing Maximum Technique
Deep learning researchers have a keen interest in proposing new novel activation functions that can boost neural network performance.
SMU: smooth activation function for deep networks using smoothing maximum technique
A good choice of activation function can have significant consequences in improving network performance.
SAU: Smooth activation function using convolution with approximate identities
Well-known activation functions like ReLU or Leaky ReLU are non-differentiable at the origin.
ErfAct and Pserf: Non-monotonic Smooth Trainable Activation Functions
An activation function is a crucial component of a neural network that introduces non-linearity in the network.
Orthogonal-Padé Activation Functions: Trainable Activation functions for smooth and faster convergence in deep networks
We have proposed orthogonal-Pad\'e activation functions, which are trainable activation functions and show that they have faster learning capability and improves the accuracy in standard deep learning datasets and models.
EIS -- a family of activation functions combining Exponential, ISRU, and Softplus
In recent years, several novel activation functions arising from these basic functions have been proposed, which have improved accuracy in some challenging datasets.
TanhSoft -- a family of activation functions combining Tanh and Softplus
Deep learning at its core, contains functions that are composition of a linear transformation with a non-linear function known as activation function.
