Processing of sequential data via machine learning models featuring temporal residual connections

Abstract

Systems and methods can include or leverage a machine-learned model (e.g., a convolutional neural network) that includes one or more temporal residual connections. In particular, each temporal residual connection can respectively supply one or more sets of intermediate feature data generated by a current instantiation of the model from a current sequential input to one or more other instantiations of the machine-learned model applied to process one or more other sequential inputs. For example, the other instantiations of the machine-learned model can include subsequent instantiations of the machine-learned model applied to process one or more subsequent sequential inputs that follow the current sequential input in a sequence and/or preceding instantiations of the machine-learned model applied to process one or more preceding sequential inputs that precede the current sequential input in a sequence.