  Transformer layers, which use an alternating pattern of multi-head attention
and multi-layer perceptron (MLP) layers, provide an effective tool for a
variety of machine learning problems. As the transformer layers use residual
connections to avoid the problem of vanishing gradients, they can be viewed as
the numerical integration of a differential equation. In this extended
abstract, we build upon this connection and propose a modification of the
internal architecture of a transformer layer. The proposed model places the
multi-head attention sublayer and the MLP sublayer parallel to each other. Our
experiments show that this simple modification improves the performance of
transformer networks in multiple tasks. Moreover, for the image classification
task, we show that using neural ODE solvers with a sophisticated integration
scheme further improves performance.
