Hello everyone,
I would like a NN to learn how to sample from a complex distribution in high-dimensions (e.g. 10-100K dimensions).
I guess that would be possible via learning parameters of a multivariate normal distribution e.g. MultivariateNormal
However, in high-dimensional problems, the parameter-size becomes too much and this becomes memory inefficient.
Can you please suggest to me an alternative if it exists in the framework or another method in order for achieving this?
Note: The scalability of the method is the most crucial aspect for me, the resulting expressiveness is not that important.
Thank you very much.
Sincerely,
Kamer