ICML 2015poster18 citations
Efficient Training of LDA on a GPU by Mean-for-Mode Estimation
Jean-Baptiste Tristan, Joseph Tassarotti, Guy Steele
Abstract
We introduce Mean-for-Mode estimation, a variant of an uncollapsed Gibbs sampler that we use to train LDA on a GPU. The algorithm combines benefits of both uncollapsed and collapsed Gibbs samplers. Like a collapsed Gibbs sampler — and unlike an uncollapsed Gibbs sampler — it has good statistical performance, and can use sampling complexity reduction techniques such as sparsity. Meanwhile, like an uncollapsed Gibbs sampler — and unlike a collapsed Gibbs sampler — it is embarrassingly parallel, and can use approximate counters.
BibTeX
@InProceedings{pmlr-v37-tristan15,
title = {Efficient Training of LDA on a GPU by Mean-for-Mode Estimation},
author = {Tristan, Jean-Baptiste and Tassarotti, Joseph and Steele, Guy},
booktitle = {Proceedings of the 32nd International Conference on Machine Learning},
pages = {59--68},
year = {2015},
editor = {Bach, Francis and Blei, David},
volume = {37},
series = {Proceedings of Machine Learning Research},
address = {Lille, France},
month = {07--09 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v37/tristan15.pdf},
url = {https://proceedings.mlr.press/v37/tristan15.html},
abstract = {We introduce Mean-for-Mode estimation, a variant of an uncollapsed Gibbs sampler that we use to train LDA on a GPU. The algorithm combines benefits of both uncollapsed and collapsed Gibbs samplers. Like a collapsed Gibbs sampler — and unlike an uncollapsed Gibbs sampler — it has good statistical performance, and can use sampling complexity reduction techniques such as sparsity. Meanwhile, like an uncollapsed Gibbs sampler — and unlike a collapsed Gibbs sampler — it is embarrassingly parallel, and can use approximate counters.}
}