Improved Processing of Sequential Data via Machine Learning Models Featuring Temporal Residual Connections

A computing system for improved temporal processing of sequential data, the computing system comprising:

• one or more processors;
• and
• one or more non-transitory computer-readable media that collectively store:
• a machine-learned convolutional neural network that comprises one or more temporal residual connections that respectively supply one or more sets of intermediate feature data generated from a current sequential input to one or more other instantiations of the machine-learned convolutional neural network applied to process one or more other sequential inputs;
• and
• instructions that, when executed by the one or more processors, cause the computing system to perform operations, the operations comprising:
• for each of a plurality of sequential inputs included in a sequence:
• processing the current sequential input with at least a portion of a current instantiation of the machine-learned convolutional neural network to generate a current set of intermediate feature data;
• storing the current set of intermediate feature data for provision to one or more subsequent instantiations of the machine-learned convolutional neural network applied to process one or more subsequent sequential inputs that are subsequent to the current sequential input in the sequence;
• accessing one or more sets of preceding intermediate feature data generated by one or more preceding instantiations of the machine-learned convolutional neural network applied to process one or more preceding sequential inputs that precede the current sequential input in the sequence;
• and
• generating a model output from the current instantiation of the machine-learned convolutional neural network based at least in part on the current set of intermediate feature data and the one or more sets of preceding intermediate feature data.