tensorflow / tensorflow/probability
Learning to Sample from Complex Distribution (High-Dimensional)
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Description
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. MultivariateNormalTriL
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
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Research direction
The issue mentions MultivariateNormalTriL but names no source file, test, or implementation entry point. Start by reviewing the existing distribution APIs and how their parameters scale with dimensionality. A contribution would need a clearly scoped alternative for high-dimensional sampling, along with defined scalability and correctness criteria.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- tensorflow
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100