Google patents neural network design that shares data across time steps

1. 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 a plurality of other instantiations of the machine-learned convolutional neural network applied to process a plurality of 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 a plurality of subsequent instantiations of the machine-learned convolutional neural network applied to process a plurality of subsequent sequential inputs that are subsequent to the current sequential input in the sequence; accessing a plurality of sets of preceding intermediate feature data generated by a plurality of preceding instantiations of the machine-learned convolutional neural network applied to process a plurality of preceding sequential inputs that precede the current sequential input in the sequence, wherein the plurality of sets of preceding intermediate feature data comprise feature maps from a plurality of different previous timestamps; 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 plurality of sets of preceding intermediate feature data.