But I would wager that those are poor implementations of automatic differentiation, as the property that automatic differentiation works with for loops, if statements, etc. is inherent to automatic differentiation. So differential programming seems like automatic differentiation just implemented properly.
I've written some simple forward-mode automatic differentiation implementations in a few languages, and it's akin to just using a library. It doesn't seem like a paradigm to me.
Reverse-mode automatic differentiation is basically backpropagation. So again, ML seems to just like to rename things. I studied mathematics and have recently been trying to learn some ML. It's pretty annoying that a lot of mathematical terms seem to have been renamed or coopted for something else.
I've written some simple forward-mode automatic differentiation implementations in a few languages, and it's akin to just using a library. It doesn't seem like a paradigm to me.
Reverse-mode automatic differentiation is basically backpropagation. So again, ML seems to just like to rename things. I studied mathematics and have recently been trying to learn some ML. It's pretty annoying that a lot of mathematical terms seem to have been renamed or coopted for something else.