I mean we didn't know autodifferentiation was a thing, so we (my advisor, not me) analytically solved our loss function for its partial derivatives. After I wrote up my thesis, I spent a lot of time learning mathematica and advanced calculus.
I haven't invested the time to take the loss function from our paper and implement in a modern framework, but IIUC, I wouldn't need to provide the derivatives manually. That would be a satisfying outcome (indicating I had wasted a lot of effort learning math that simply wasn't necessary, because somebody had automated it better than I could do manually, in a way I can understand more easily).
it means that code has to read values from each layer and do some summarizing math, instead of passing layer blocks to a graphics card in one primitive operation implemented on the card.
No. I should have said "determined the partial derivatives of the weights with respect to the variables analytically". We didn't have layers- the whole architecture was a truly crazy combination of dynamic programming with multiple different matrices and a loss function that combined many different types of evidence. AFAICT nobody does any of this any more for finding genes. We just take enormous amounts of genetic data and run an autoencoder or a sequence model over it.
