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 [email protected]

Citation

• Paper link: [http://proceedings.mlr.press/v70/zhang17a.html)

@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},
}