》 资源 》论文》A trust-region method for stochastic variational inference with applications to streaming data
A trust-region method for stochastic variational inference with applications to streaming data
| 
45 | 
42 | 
0
Abstract
Stochastic variational inference allows for fast posterior inference in complex Bayesian models. However, the algorithm is prone to local optima which can make the quality of the posterior approximation sensitive to the choice of hyperparameters and initialization. We address this problem by replacing the natural gradient step of stochastic varitional inference with a trust-region update. We show that this leads to generally better results and reduced sensitivity to hyperparameters. We also describe a new strategy for variational inference on streaming data and show that here our trust-region method is crucial for getting good performance.
还没有评论，说两句吧！
热门资源
Learning to learn...
The move from hand-designed features to learned...
A Mathematical Mo...
Direct democracy, where each voter casts one vo...
Stratified Strate...
In this paper we introduce Stratified Strategy ...
Rating-Boosted La...
The performance of a recommendation system reli...
Hierarchical Task...
We extend hierarchical task network planning wi...
智能在线
400-630-6780
聆听.建议反馈
E-mail: support@tusaishared.com
资源分类
