Abstract: The present disclosure provides systems, methods, and computer program products for modeling dependencies throughout a network using a global-self attention model with a content attention layer and a positional attention layer that operate in parallel. The model receives input data comprising content values and context positions. The content attention layer generates one or more output features for each context position based on a global attention operation applied to the content values independent of the context positions. The positional attention layer generates an attention map for each of the context positions based on one or more content values of the respective context position and associated neighboring positions. Output is determined based on the output features generated by the content attention layer and the attention map generated for each context position by the positional attention layer. The model improves efficiency and can be used throughout a deep network.