https://github.com/cossio/rbmsannealedimportancesampling.jl

https://github.com/cossio/rbmsannealedimportancesampling.jl

Last synced: 10 days ago
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• Host: GitHub
• URL: https://github.com/cossio/rbmsannealedimportancesampling.jl
• Owner: cossio
• License: mit
• Created: 2022-12-05T15:25:25.000Z (almost 2 years ago)
• Default Branch: master
• Last Pushed: 2024-06-24T11:59:50.000Z (5 months ago)
• Last Synced: 2024-10-07T14:30:50.783Z (about 1 month ago)
• Language: Julia
• Size: 592 KB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • License: LICENSE.md
  • Citation: CITATION.bib

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README

        
# RBMsAnnealedImportanceSampling Julia package

Annealed importance sampling algorithm to estimate the partition function of Restricted Boltzmann machines. The package is registered. Install it with:

```julia

import Pkg

Pkg.add("RBMsAnnealedImportanceSampling")

```

## Related packages

Restricted Boltzmann machines in Julia:

- https://github.com/cossio/RestrictedBoltzmannMachines.jl

Use RBMs on the GPU (CUDA):

- https://github.com/cossio/CudaRBMs.jl

Centered RBMs:

- https://github.com/cossio/CenteredRBMs.jl

## Citation

If you use this package in a publication, please cite:

* Jorge Fernandez-de-Cossio-Diaz, Simona Cocco, and Remi Monasson. "Disentangling representations in Restricted Boltzmann Machines without adversaries." Physical Review X 13, 021003 (2023).

Or you can use the included [CITATION.bib](https://github.com/cossio/RestrictedBoltzmannMachines.jl/blob/master/CITATION.bib).