Code for the paper : Analysis of learning a flow-based generative model from limited sample complexity (link to paper)
(Figs. 1, 2)
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Theory.ipynb provides a Jupyter notebook implementation of the theoretical characterization of Result III.1 for the summary statistics tracking the generative flow. Any choice of schedule functions
$\alpha(t), \beta(t)$ can be specified as an input.
(Fig. 2)
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Simulations.py implements the associated numerical experiments for the flow-based model. The DAE is trained using Pytorch. For instance, to run the training on
$n=10$ samples from a target binary Gaussian mixture with centroid norm$\lVert \mu\lVert=1.5$ and cluster variance$\sigma=0.5$ , run
python3 Simulations.py --n 10 --m 1.5 --s 0.5
Versions: These notebooks employ Python 3.12 .