Neural Network Activation Scaled Clipping Layer

Abstract

Activation scaled clipping layers for neural networks are described. An activation scaled clipping layer processes an output of a neuron in a neural network using a scaling parameter and a clipping parameter. The scaling parameter defines how numerical values are amplified relative to zero. The clipping parameter specifies a numerical threshold that causes the neuron output to be expressed as a value defined by the numerical threshold if the neuron output satisfies the numerical threshold. In some implementations, the scaling parameter is linear and treats numbers within a numerical range as being equivalent, such that any number in the range is scaled by a defined magnitude, regardless of value. Alternatively, the scaling parameter is nonlinear, which causes the activation scaled clipping layer to amplify numbers within a range by different magnitudes. Each scaling and clipping parameter is learnable during training of a machine learning model implementing the neural network.

Dependencies are inferred from the claim text.