Skip to content

hao-w/apgs

master
Switch branches/tags
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
 
 
 
 

Source code for the amortized population Gibbs (APG) samplers in the paper Amortized Population Gibbs Samplers with Neural Sufficient Statstics, which was accepted by ICML2020 (paper; video). The implementation was built on ProbTorch, a deep generative modeling library which extends PyTorch.

Prerequisite

  1. Install PyTorch >= 1.6.0 [instructions]
  2. Install ProbTorch [instructions]

Training Instructions

Each set of experiments are stored in its own subdirectory. In detail, gmm, dmm, bmnist correspond the GMM clustering task, Deep Generative Mixture Model clustering task, and the Bouncing MNIST trakcing task, respectively. Each subdirectory includes the implementation of APG sampler, baselines, and evaluation functions that are put in jupyter notebooks for the convenience of interactivity.

To train the APG sampler, go to each subdirectory and run

python apg_training.py

If you have any questions about the code or the paper, please feel free to contact me by wu.hao10@northeastern.edu.

About

Amortized Population Gibbs Samplers with Neural Sufficient Statistics

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published