The mathematical proposition known as the chain rule and its applied form, backpropagation, share many similarities in the context of neural networks. The chain rule enables us to decompose the gradient into a product of terms, while backpropagation is the efficient implementation of this proposition in real networks.

Consider this, if you were to write out the compute graph, backpropagation is essentially equivalent to multiplying by the transpose of the local Jacobians. These Jacobians represent the partial derivatives of each intermediate operation with respect to its inputs. This operation brings us to the concept of 'reverse-mode autodiff', a fascinating way of embedding backpropagation into an object-oriented neural network structure. If you wish to delve deeper into this, there's a tiny autodiff library that might interest you, micrograd, which provides an excellent hands-on understanding of PyTorch.

Chain rule and backpropagation go hand in hand when it comes to their application in neural networks. If you wish to explore this further, a Python implementation of backpropagation, as part of a homework assignment from Andrew Ng's Stanford course, is available here.

To fully understand the computational optimization, one must comprehend the importance of the order of multiplications in these operations. For instance, by using backpropagation (or reverse-mode differentiation), we can avoid the computationally expensive operation of multiplying an n x n matrix with an n x n² matrix.

Backpropagation might seem like a simple application of the chain rule, but it carries a few optimizations. By expressing a neural network as a combination of functions, the chain rule implies that the gradient of one layer can be calculated with the gradient of the next layer, hence leading to efficient calculations. For further reading, consider exploring this blog on forward vs reverse-mode autodiff.

For more insights into the relationship between the chain rule and backpropagation, this resource provides excellent overviews.

The recent discussion raises important concerns about the lack of key paper citations, particularly regarding the parallel structure in Transformer layers. It's worth noting that this concept was first proposed in the paper "MUSE: Parallel Multi-Scale Attention for Sequence to Sequence Learning" (see Formula 2). Further, the notion of merging linear layers of the MLP and self-attention to enhance time efficiency was discussed in Section 3.5.

One of the points in the discussion is the … click here to read