Deep neural network training method and system, and causality discovery method

A method of detecting causality between input variables using a deep neural network, which is performed by a computer including a memory and a processor, the method comprising operations of:

• inputting an input value of training data acquired from n input variables (n is a natural number greater than or equal to 2) to an input layer of a first neural network, which is based on a graph neural network, and calculating a predicted value through an output layer;
• training the first neural network on the basis of first training information, which is a result of comparing the predicted value to a target value of the training data;
• receiving an intermediate value in an lth hidden layer (l is a natural number greater than or equal to 1) of the first neural network from a second neural network, which is based on a deep neural network, and calculating an intermediate point value between a point at which the input value is observed and a point at which the target value is observed;
• training the first and second neural networks on the basis of second training information based on similarity between the intermediate point value and the input value of the training data;
• and
• repeatedly training the first and second neural networks a preset maximum number of training times;
• and
• providing an adjacency matrix of the trained first neural network,
• wherein the adjacency matrix has a size corresponding to the square of the number (n) of input variables and has an element value with causality between 0 and 1, which is expressed relatively according to the strength of causality relationships between the input variables.

A deep neural network-based system for detecting causality between input values, the deep neural network-based system comprising:

• a memory in which a program for detecting the causality between the input values on the basis of training data acquired from n input variables (n is a natural number greater than or equal to 2) is stored;
• and
• a processor configured to execute the program stored in the memory, wherein when the program is executed,
• the processor inputs an input value of training data to an input layer of a first neural network, which is based on a graph neural network, calculates a predicted value through an output layer, and trains the first neural network on the basis of first training information, which is a result of comparing the predicted value to a target value of the training data, and
• the processor receives an intermediate value in an lth hidden layer (l is a natural number greater than or equal to 1) of the first neural network from a second neural network, which is based on a deep neural network, calculates an intermediate point value between a point at which the input value is observed and a point at which the target value is observed, and trains the first and second neural networks on the basis of second training information based on similarity between the intermediate point value and the input value of the training data.