"[...] modern neural network (NN) architectures have complex designs with many components [...]"
I find the Transformer architecture actually very simple compared to previous models like LSTMs or other recurrent models. You could argue that their vision counterparts like ViT are conceptually maybe even simpler than ConvNets?
Also, can someone explain why they are so keen to remove the skip connections? At least when it comes to coding, nothing is simpler than adding a skip connection and computationally the effect should be marginal?
I find the Transformer architecture actually very simple compared to previous models like LSTMs or other recurrent models. You could argue that their vision counterparts like ViT are conceptually maybe even simpler than ConvNets?
Also, can someone explain why they are so keen to remove the skip connections? At least when it comes to coding, nothing is simpler than adding a skip connection and computationally the effect should be marginal?