Information processing apparatus, arithmetic method, and non-temporary computer-readable medium

A method of causing one or more processors to execute arithmetic processing using a machine learning model of a vector neural network type, wherein

• the machine learning model has
• a plurality of vector neuron layers each including a plurality of nodes,
• when one of the plurality of vector neuron layers is referred to as an upper layer and a vector neuron layer below the upper layer is referred to as a lower layer,
• the method causing the one or more processors to execute outputting one output vector by using output vectors from the plurality of nodes of the lower layer as an input for each node of the upper layer, the outputting including:
• when any node of the upper layer is referred to as a target node,
• (a) obtaining a prediction vector based on a product of the output vector of each node of the lower layer and a prediction matrix,
• (b) obtaining a sum vector based on a linear combination of the prediction vectors obtained from each node of the lower layer,
• (c) obtaining a normalization coefficient by normalizing a norm of the sum vector, and
• (d) obtaining the output vector of the target node by dividing the sum vector by the norm and then multiplying the divided sum vector by the normalization coefficient.

A non-temporary computer-readable medium that stores instructions for causing one or more processors to execute arithmetic processing using a machine learning model of a vector neural network type, wherein

• the machine learning model has
• a plurality of vector neuron layers each including a plurality of nodes,
• when one of the plurality of vector neuron layers is referred to as an upper layer and a vector neuron layer below the upper layer is referred to as a lower layer,
• the instructions causing the one or more processors to execute outputting one output vector by using output vectors from the plurality of nodes of the lower layer as an input for each node of the upper layer, the outputting including:
• when any node of the upper layer is referred to as a target node,
• (a) obtaining a prediction vector based on a product of the output vector of each node of the lower layer and a prediction matrix,
• (b) obtaining a sum vector based on a linear combination of the prediction vectors obtained from each node of the lower layer,
• (c) obtaining a normalization coefficient by normalizing a norm of the sum vector, and
• (d) obtaining the output vector of the target node by dividing the sum vector by the norm and then multiplying the divided sum vector by the normalization coefficient.