juliusberner/sde_sampler
Improved sampling via learned diffusions (ICLR2024) and an optimal control perspective on diffusion-based generative modeling (TMLR2024)
This project helps researchers and practitioners generate diverse, high-quality samples from complex, unnormalized probability distributions. You provide an unnormalized target density, and the project outputs samples that follow that distribution, enabling tasks like Monte Carlo simulations or generative modeling. It's designed for quantitative analysts, statisticians, or machine learning researchers working with intricate data distributions.
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Use this if you need to generate samples from a probability distribution whose mathematical form is known but whose normalization constant is intractable, and you're looking for advanced, diffusion-based sampling techniques.
Not ideal if you're looking for a simple, off-the-shelf sampling method for standard distributions or if you don't have a technical understanding of stochastic differential equations and neural network-based control.
Stars
74
Forks
10
Language
Python
License
MIT
Category
Last pushed
Mar 14, 2025
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