jonathancrabbe / symbolic-pursuit Goto Github PK

Hi @JonathanCrabbe,

Thanks for this wonderful work.
I read your paper and supplementary material, have a couple of doubts, your help would be appreciated to understand these concepts :(please correct me if I'm missing anything)

• Meijer G-functions is applied to the contour of integration, which is a task-dependent factor, how exactly such hyperparameters are estimated (how to select the subset for p, q, m, n)?
  • For example, if I want to interpret CNN's how do I define this contour?
• All the selected tasks in the paper seem to be regression (univariable output) problems, Can you provide some pointers to extend this work on multi-output G-functions? (can you suggest some literature for the same)
• I tried to reproduce some of the experiments, could run Synthetic data exp. easily (and could easily observe the improvements of this method w.r.t LIME), but for the 'wine_quality' and 'Boston housing' datasets it was taking quite a long time to complete, am I missing some installation steps or the method usually takes that long?
• The method proposed can definitely be used for interpreting models, but it can easily help in estimating the data-generation process right? (why is it restricted to model interpretability in the paper?)
• Is there any intuition on real and imaginary values of 'Z'? (like, what real curve and imaginary curve correspond to?)
• What is the reason for optimizing over residuals, when you have true predictions (on the trained network)?

Thank you,
Avinash