Finite rank deep kernel learning with linear computational complexity

A system, comprising:

• a memory comprising computer-executable instructions;
• a processor configured to execute the computer-executable instructions and cause the system to perform a finite rank deep kernel learning method, the finite rank deep kernel learning method comprising:
• receiving a training dataset;
• forming a set of embeddings by subjecting the training dataset to a deep neural network, wherein the forming comprises minimizing a loss function comprising a data fit loss component, a complexity component, and a regularity loss component;
• forming, from the set of embeddings, a plurality of dot kernels;
• linearly combining the plurality of dot kernels to for a composite kernel for a Gaussian process;
• receiving live data from an application;
• and
• predicting a plurality of values and a plurality of uncertainties associated with the plurality of values simultaneously using the composite kernel.

A non-transitory computer-readable medium comprising instructions that, when executed by a processor of a processing system, cause the processing system to perform a finite rank deep kernel learning method, the method comprising:

• receiving a training dataset;
• forming a set of embeddings by subjecting the training dataset to a deep neural network, wherein the forming comprises minimizing a loss function comprising a data fit loss component, a complexity component, and a regularity loss component;
• forming, from the set of embeddings, a plurality of dot kernels;
• linearly combining the plurality of dot kernels to for a composite kernel for a Gaussian process;
• receiving live data from an application;
• and
• predicting a plurality of values and a plurality of uncertainties associated with the plurality of values simultaneously using the composite kernel.