Machine-learning techniques for representing items in a spectral domain

One or more non-transitory computer readable media including instructions that, when executed by one or more processors, cause the one or more processors to represent items in a spectral domain by performing the steps of:

• executing a first neural network on a first set of data points associated with both a first item and the spectral domain to generate a second neural network;
• generating a first set of predicted data points that are associated with both the first item and the spectral domain via the second neural network;
• and
• generating a trained machine learning model based on the first neural network, the second neural network, and the first set of predicted data points,
• wherein the trained machine learning model maps one or more positions within the spectral domain to one or more values associated with an item based on a set of data points associated with both the item and the spectral domain.

A system comprising:

• one or more memories storing instructions;
• and
• one or more processors coupled to the one or more memories that, when executing the instructions, perform the steps of:
• executing a first neural network on a first set of data points associated with both a first item and a spectral domain to generate a second neural network;
• generating a first set of predicted data points that are associated with both the first item and the spectral domain via the second neural network;
• and
• generating a trained machine learning model based on the first neural network, the second neural network, and the first set of predicted data points,
• wherein the trained machine learning model maps one or more positions within the spectral domain to one or more values associated with an item based on a set of data points associated with both the item and the spectral domain.