horenbergerb/BernoulliDiffusion

Simple diffusion implementation for binary-valued data using a Bernoulli distribution instead of a Gaussian.

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Emerging

This tool helps machine learning engineers or researchers generate new, synthetic binary data that looks just like their existing datasets. You provide it with a collection of binary sequences (composed of 1s and 0s), and it learns the patterns to produce an unlimited number of new sequences with similar characteristics. It's designed for those working with fixed-length binary data.

No commits in the last 6 months.

Use this if you need to create more binary data samples that mimic the distribution of an existing binary dataset, for tasks like data augmentation or generating synthetic examples.

Not ideal if your data is not binary (e.g., continuous numbers, images, or text) or if you need to generate data that varies in length.

synthetic-data-generation binary-data machine-learning-research data-augmentation time-series-generation
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 6 / 25
Maturity 16 / 25
Community 15 / 25

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Stars

22

Forks

5

Language

Python

License

MIT

Last pushed

Nov 14, 2022

Commits (30d)

0

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