Clarification on the rules


thank you very much for organizing this challenge !
I have a few questions that I hope you can help me with, to avoid infringing any rules:

  1. would it be possible to have a reference equation for calculating the diversity metrics ?
  2. would it be okay to have a model generate 100/200 samples and select in a ‘post-processing’ stage the 30 that would maximize the diversity metrics ?
  3. would it be possible to encode, inside the NN, the information on the properties that x0/x1 must have (for example by fitting the network to just predict slopes and biases and using a final, non-trainable layer, to transform those 4 coefficients into 2 straight lines of 50 samples) ?

Thank you in advance !

Dear @team
would it be possible to have a clarification on these aspects of the authorized modelling strategies ? It would be of great help.

I actually have 2 further questions:

  1. Can we build an algorithm based on more than 1 NN or other ML strategies (also in recursive ways), or should we stick to only a final NN that takes some input ?
  2. Can we generate additional samples ?

Thank you.