[D] Resources for deepening knowledge of Transformers

I think I understand the basics of how transformers work, i.e. positional encodings, the idea of attention and "differentiable dictionary indexing", how they process sequences when compared to RNNs, the stack of self-attention and cross-attention layers, etc. I've also read the original paper.

I'm wondering if anyone has a good list of papers and resources that build up on this to **improved architectures** **and/or** intuitions as to **why** they work. Two parallels in CNNs, in each of those directions respectively, …

architectures attention basics building cnns dictionary etc good indexing knowledge list machinelearning paper process resnet resources self-attention stack think transformers work