Implementation for the paper "Stochastic Gradient Monomial Gamma Sampler"
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README.md

SGMGT

Implementations of the models in the paper "Stochastic Gradient Monomial Gamma Sampler" by Yizhe Zhang, Changyou Chen, Zhe Gan, Ricardo Henao, Lawrence Carin, ICML 2017

Prerequisite:

  • Theono version >= 0.8
  • CUDA version 8.0
  • cudnn

Run

  • Run: python eval_ptb_sgmgt.py for demo
  • Options: options can be made by changing the model/optimizers.py code.

Data:

  • Penn Treebank dataset

For any question or suggestions, feel free to contact yz196@duke.edu

Citation

@InProceedings{zhang17astochastic,
  title = 	 {Stochastic Gradient Monomial Gamma Sampler},
  author = 	 {Yizhe Zhang and Changyou Chen and Zhe Gan and Ricardo Henao and Lawrence Carin},
  booktitle = 	 {Proceedings of the 34th International Conference on Machine Learning},
  pages = 	 {3996--4005},
  year = 	 {2017},
  publisher = {PMLR},
}