The transformer, introduced in the paper Attention Is All You Need, is a popular new neural network architecture that is commonly viewed as an alternative to recurrent neural networks, like LSTMs and GRUs.
However, having gone through the paper, as well as several online explanations, I still have trouble wrapping my head around how they work. How can a non-recurrent structure be able to deal with inputs of arbitrary length?