Accelerating collapsed variational bayesian inference for latent dirichlet allocation with nvidia CUDA compatible devices
スポンサーリンク
概要
- 論文の詳細を見る
The original publication is available at www.springerlink.comNext-Generation Applied Intelligence: 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2009, Tainan, Taiwan, June 24-27, 2009.In this paper, we propose an acceleration of collapsed variational Bayesian (CVB) inference for latent Dirichlet allocation (LDA) by using Nvidia CUDA compatible devices. While LDA is an efficient Bayesian multi-topic document model, it requires complicated computations for parameter estimation in comparison with other simpler document models, e.g. probabilistic latent semantic indexing, etc. Therefore, we accelerate CVB inference, an efficient deterministic inference method for LDA, with Nvidia CUDA. In the evaluation experiments, we used a set of 50,000 documents and a set of 10,000 images. We could obtain inference results comparable to sequential CVB inference.
論文 | ランダム
- 柿岡と女満別のK-indexに関する調査
- "電力の鬼"九十翁の大計(インタビュー)
- On-Line Multicasting in All-Optical Networks
- 結膜研究の動向
- Preparation of Single Phase (Bi_Pb_)_2Sr_2Ca_2Cu_3O_y Films with Preferential Orientation of C-Axis by Laser Ablation Method