Machine-Learning Models Based on Non-local Neural Networks

One or more computer-readable non-transitory storage media embodying software that is operable when executed to:

• train a baseline machine-learning model based on a neural network comprising a plurality of stages, wherein each stage comprises a plurality of neural blocks;
• access a plurality of training samples comprising a plurality of content objects, respectively;
• determine one or more non-local operations, wherein each non-local operation is based on one or more pairwise functions and one or more unary functions;
• generate one or more non-local blocks based on the plurality of training samples and the one or more non-local operations;
• determine a stage from the plurality of stages of the neural network;
• and
• train a non-local machine-learning model by inserting each of the one or more non-local blocks in between at least two of the plurality of neural blocks in the determined stage of the neural network.

A system comprising:

• one or more processors;
• and a non-transitory memory coupled to the processors comprising instructions executable by the processors, the processors operable when executing the instructions to:
• train a baseline machine-learning model based on a neural network comprising a plurality of stages, wherein each stage comprises a plurality of neural blocks;
• access a plurality of training samples comprising a plurality of content objects, respectively;
• determine one or more non-local operations, wherein each non-local operation is based on one or more pairwise functions and one or more unary functions;
• generate one or more non-local blocks based on the plurality of training samples and the one or more non-local operations;
• determine a stage from the plurality of stages of the neural network;
• and
• train a non-local machine-learning model by inserting each of the one or more non-local blocks in between at least two of the plurality of neural blocks in the determined stage of the neural network.