International Business Machines Corporation

Max Biggs

Brian Leo Quanz

Pavithra Harsha

Wei Sun

Shivaram Subramanian

A machine learning system that incorporates arbitrary constraints is provided. The machine learning system selects a set of domain-specific constraints from a plurality of sets of domain-specific constraints. The machine learning system selects a set of general functional relationships from a plurality of sets of general functional relationships. The machine learning system maps the selected set of general functional relationships and the selected set of domain-specific constraints to a set of learning transforms. The machine learning system modifies a machine learning specification according to the set of learning transforms, wherein the machine learning specification specifies a model construction, a model setup, and a training objective function. The machine learning system optimizes a machine learning model according to the modified machine learning specification.

Domain-specific constraints for predictive modeling

A machine learning system that incorporates arbitrary constraints into deep learning model is provided. The machine learning system provides a set of penalty data points en a set of arbitrary constraints in addition to a set of original training data points. The machine learning system assigns a penalty to each penalty data point in the set of penalty data points. The machine learning system optimizes a machine learning model by solving an objective function based on an original loss function and a penalty loss function. The original loss function is evaluated over a set of original training data points and the penalty loss function is evaluated over the set of penalty data points. The machine learning system provides the optimized machine learning model based on a solution of the objective function.

Loss augmentation for predictive modeling

A machine learning system that uses a split net configuration to incorporate arbitrary constraints receives a set of input data and a set of functional constraints. The machine learning system jointly optimizes a deep learning model by using the set of input data and a wide learning model by using the set of constraints. The deep learning model includes an input layer, an output layer, and an intermediate layer between the input layer and the output layer. The wide learning model includes an input layer and an output layer but no intermediate layer. The machine learning system provides a machine learning model comprising the optimized deep learning model and the optimized wide learning model.

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Max Biggs