Provisioning deep learning (DL) models that preserve relationships between response variables and selected explanatory variables

A system, comprising:

• one or more processors;
• and
• a computer-readable storage device coupled to the one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations for training deep learning (DL) models, the operations comprising:
• training a denoising stacked autoencoder (DAE) using a noisy training dataset comprising a noisy sub-set and a non-noisy sub-set;
• wherein the noisy sub-set is generated by:
• selecting a pre-defined percentage of training samples from a training dataset and copying the selected training samples into the noisy sub-set, and
• randomly adjusting data attributes of the selected training samples across rows to introduce noise;
• providing an artificial neural network (ANN) comprising multiple hidden layers,
• wherein at least one hidden layer comprising at least a portion of an encoder of the trained DAE, and
• wherein the at least a portion of the encoder comprising parameters of the at least one hidden layer determined during training of the DAE;
• training the ANN using the training dataset;
• and
• providing a version of the ANN for inference.

Computer-readable storage media coupled to the one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations for training deep learning (DL) models, the operations comprising:

• training a denoising stacked autoencoder (DAE) using a noisy training dataset comprising a noisy sub-set and a non-noisy sub-set;
• wherein the noisy sub-set is generated by:
• selecting a pre-defined percentage of training samples from a training dataset and copying the selected training samples into the noisy sub-set, and
• randomly adjusting data attributes of the selected training samples across rows to introduce noise;
• providing an artificial neural network (ANN) comprising multiple hidden layers,
• wherein at least one hidden layer comprising at least a portion of an encoder of the trained DAE, and
• wherein the at least a portion of the encoder comprising parameters of the at least one hidden layer determined during training of the DAE;
• training the ANN using the training dataset;
• and
• providing a version of the ANN for inference.