In "Attention Is All You Need", why are the FFNs in (2) the same as two convolutions with kernel size 1?
Convolution is actually applying a sliding window projection over a tensor. With the kernel size of 1, the convolution reduces a to a for loop over the vectors, i.e., each of the vectors gets multiplied by a filter matrix. But this is actually a definition of matrix multiplication which is how feed-forward layers are implemented.