Can you learn an algorithm from a trained model? Are there any papers where an algorithm was entirely based on the results of a trained model? Let me explain. 

Suppose you want to come up with an algorithm that sorts three numbers $a,b,c$. I can generate several training data points $x_i = (a_i, b_i, c_i), a_i, b_i, c_i \in \mathbb{R}$ with their label $\hat y_i = (\min(x_i), mid(x_i), \max(x_i))$. That way, I can generate a lot of data points and train a model to predict them in their order. 

My question is, are there any papers which were able to translate the trained model back to an algorithm that is understandable by humans (instead of just the values of the parameters ?). I'd be very interested even if the algorithms were very simple.
In this article https://arxiv.org/abs/1711.09784 the authors the fits decision tree to a neural network in order to understand what is the neural network does. Is it what you are looking for?