Method and system for efficient neural network training

摘要

Training method for a machine learning, ML, model based on a neural-symbolic framework that is a hybrid of a symbolic reasoning algorithm and artificial neural networks, providing a method of training multiple artificial neural networks in a ML which uses abductive reasoning to improve training data for a neural network by abducting possible correct intermediate neuron outputs (i.e. intermediate labels). The method comprising: receiving training data, i.e. input data and a correct output label, and a corresponding set of logical rules; inputting the label and the set of logical rules to a logic module of the ML model; using abduction at the logic module to compute a set of possible abducted intermediate labels for the input data item of the pair; and, training, iteratively, neural network modules of the ML model by: inputting the training data to neural models; outputting intermediate labels; comparing the output intermediate labels to the abducted intermediate labels and determining how well they match with other; using backpropagation to maximise the match between the abducted intermediate labels and the intermediate labels. The training data may be an image and a final label.