This project features some exploration to get a fully parameterized Gaussianization scheme. There is a big normalizing flows community with many different algorithms for density estimation and sampling. There is also a relatively small community using Gaussianization and density destructors for other applications including information theory measures. This is an attempt to bridge the two communities together.
This project was inspired by:
- RBIG - original algorithm
- Gaussianization Flows - the fully parameterized method.
- nflows - the research normalizing flows library.
- Original Exploration -
Here is an example where we show the original data and the data generated by a trained Gaussianization Flow model.
| Original Data | Generated Samples |
|---|---|
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The same Gaussianization Flow model with the initial Latent space versus the latent space after trained.
| Original Latent Space | Trained Latent Space |
|---|---|
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Here is an example where we show the original data and the data generated by a trained Gaussianization Flow model.
| Original Data | Generated Samples |
|---|---|
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...and the probabilities of the dataset.
| Original Data |
|---|
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The same Gaussianization Flow model with the initial Latent space versus the latent space after trained.
| Original Latent Space | Trained Latent Space |
|---|---|
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This work was supported by the European Research Council (ERC) Synergy Grant “Understanding and Modelling the Earth System with Machine Learning (USMILE)” under Grant Agreement No 855187.