beneboeck/sparse-bayesian-gen-mod
Source code of the Paper "Sparse Bayesian Generative Modeling for Compressive Sensing" (NeurIPS 24)
This project helps researchers and engineers reconstruct high-fidelity signals or images from very limited, noisy measurements. It takes compressed and noisy data samples as input and outputs a reconstructed signal with quantified uncertainty. This is useful for scientists and engineers working with data acquisition systems that are constrained by cost, time, or physical limitations.
No commits in the last 6 months.
Use this if you need to recover full data from sparse, incomplete, or corrupted sensor readings, especially when you have only a few training examples.
Not ideal if your application requires commercial use, as this software is restricted to non-commercial research without explicit permission.
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Python
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Last pushed
May 30, 2025
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