As part of neural network sensitivity analyses, 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.
Please help me write a proper abstract based on the patent claims. CLAIM: 1-11. (canceled) 12. A method for implementation by one or more data processors of at least one computing system comprising: inputting, an input variable for an input layer node forming part of a neural network, the input variable being extracted from a record, the input variable into the input layer node to generate a base output for a first hidden layer node forming part of the neural network and to generate a non-perturbed output of the neural network; perturbing, for the record, the input variable to generate a perturbed input layer output of the input layer node; generating, for the record, a perturbed hidden layer output for the first hidden layer node by reusing the generated base output and the perturbed input layer output; initiating sensitivity analyses of the neural network sensitivity by comparing the non-perturbed output with the perturbed hidden layer output. 13. A method as in claim 12, wherein the base outputs ai are determined using: a i = ∑ j = 1 N   w ij  x j + b i where i is an index of the hidden layer nodes, j is an index of input variables, x is the input variable, wij is weight from input node j to hidden node i, and bi is bias for hidden node i. 14. A method as in claim 13, wherein for a perturbed input variable k that is perturbed by Δ, the output for the hidden layer node that reuses the corresponding cached generated base output uses: a i  ( k ) = ∑ j = 1 N   w ij  x j + b i + Δ   x k * w ik = a i + Δ   x k * w ik 15. A method as in claim 12, further comprising: caching the generated base output for the hidden layer node; wherein the cached generated base output are reused. 16. A method as in claim 12, wherein at least one of the inputting, perturbing, generating, and initiating is implemented by at least one data processor. 17. A non-transitory computer program product storing instructions which when executed by at least one data processor of at least one computing system result in operations comprising: inputting, an input variable for an input layer node forming part of a neural network, the input variable being extracted from a record, the input variable into the input layer node to generate a base output for a first hidden layer node forming part of the neural network and to generate a non-perturbed output of the neural network; perturbing, for the record, the input variable to generate a perturbed input layer output of the input layer node; generating, for the record, a perturbed hidden layer output for the first hidden layer node by reusing the generated base output and the perturbed input layer output; initiating sensitivity analyses of the neural network sensitivity by comparing the non-perturbed output with the perturbed hidden layer output. 18. A method as in claim 17, wherein the base outputs ai are determined using: a i = ∑ j = 1 N   w ij  x j + b i where i is an index of the hidden layer nodes, j is an index of input variables, x is the input variable, wij is weight from input node j to hidden node i, and bi is bias for hidden node i. 19. A method as in claim 18, wherein for a perturbed input variable k that is perturbed by Δ, the output for the hidden layer node that reuses the corresponding cached generated base output uses: a i  ( k ) = ∑ j = 1 N   w ij  x j + b i + Δ   x k * w ik = a i + Δ   x k * w ik 20. A method as in claim 17, further comprising: caching the generated base output for the hidden layer node; wherein the cached generated base output are reused. 21. A method as in claim 17, wherein at least one of the inputting, perturbing, generating, and initiating is implemented by at least one data processor. 22. A method as in claim 17, further comprising: generating an array of perturbed scores, the perturbed scores based on a single input variable perturbation that resulted in the generated perturbed hidden layer output; and comparing the array of perturbed scores to non-perturbed scores based on the non-perturbed output to determine the sensitivity of the neural network. 23. A method for analyzing the sensitivity of a neural network implemented by one or more data processors of at least one computing system comprising: generating, by at least one of the one or more data processors, base output for a first hidden node of a neural network using a non-perturbed input variable; caching the generated base output for the first hidden layer node; generating an output of the neural network using a perturbed input variable and by reusing the cached base output; and providing data comprising the output. 24. The method of claim 10, wherein providing the data comprises at least one of: displaying the data, transmitting the data to a remote computing device, loading the data into memory, and storing the data.