Reduction of Computation Complexity of Neural Network Sensitivity Analysis

摘要

As part of neural network sensitivity analysis, base outputs of hidden layer nodes of a neural network model for non-perturbed variables can be reused when perturbing the variables. Such an arrangement greatly reduces complexity of the calculations required to generate outputs of the model. Related apparatus, systems, techniques and articles are also described.

主权项

1. A method for implementation by one or more data processors of at least one computing system comprising:
receiving a plurality of records, each record comprising input variables for a plurality of input layer nodes forming part of a neural network; inputting, for each record, the input variables into the input layer nodes to generate a base output for each of a plurality of first hidden layer nodes forming part of the neural network and to generate a non-perturbed output of the neural network; perturbing, for each record, each input variable; generating, for each record, an output for the first hidden layer nodes by reusing the corresponding generated base outputs and a perturbed output of the neural network; initiating sensitivity analysis of the neural network sensitivity by comparing the non-perturbed outputs with the perturbed outputs across the input layer nodes.