Creating aligned machine learning models through bootstrapping with attention

A system for resource-efficient machine learning model configuration, comprising:

• one or more processors;
• and
• a memory comprising instructions that, when executed by the one or more processors, cause the system to:
• divide a set of labeled training data into training data subsets;
• train a first machine learning model using a first training data subset of the training data subsets;
• train a second machine learning model that has a same architecture as the first machine learning model using a second training data subset of the training data subsets;
• create an aligned weight matrix based on weights of the trained first machine learning model and the trained second machine learning model;
• and
• configure an aligned machine learning model that has the same architecture as the first machine learning model using the aligned weight matrix.

A non-transitory computer readable medium comprising instructions that, when executed by one or more processors of a computing system, cause the computing system to:

• divide a set of labeled training data into training data subsets;
• train a first machine learning model using a first training data subset of the training data subsets;
• train a second machine learning model that has a same architecture as the first machine learning model using a second training data subset of the training data subsets;
• create an aligned weight matrix based on weights of the trained first machine learning model and the trained second machine learning model;
• and
• configure an aligned machine learning model that has the same architecture as the first machine learning model using the aligned weight matrix.