[1][2][3][4][5] This allows for gradient-based optimization of parameters in the program, often via gradient descent, as well as other learning approaches that are based on higher order derivative information.
[7] Attempts generally fall into two groups: The use of Just-in-Time compilation has emerged recently as a possible solution to overcome some of the bottlenecks of interpreted languages.
The C++ heyoka and python package heyoka.py make large use of this technique to offer advanced differentiable programming capabilities (also at high orders).
Newer approaches resolve this issue by constructing the graph from the language's syntax or IR, allowing arbitrary code to be differentiated.
These applications demonstrate the potential of differentiable programming in contributing to significant advancements in understanding complex biological systems and improving healthcare solutions.