Name
Abstract | ||
|---|---|---|
We develop a sampling technique for Hierarchical Dirichlet process models. The parallel algorithm builds upon [1] by proposing large split and merge moves based on learned sub-clusters. The additional global split and merge moves drastically improve convergence in the experimental results. Furthermore, we discover that cross-validation techniques do not adequately determine convergence, and that previous sampling methods converge slower than were previously expected. |
Year | Venue | Field |
|---|---|---|
2014 | ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 27 (NIPS 2014) | Convergence (routing),Hierarchical Dirichlet process,Parallel algorithm,Computer science,Algorithm,Artificial intelligence,Sampling (statistics),Merge (version control),Machine learning |
DocType | Volume | ISSN |
Conference | 27 | 1049-5258 |
Citations | PageRank | References |
6 | 0.50 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jason Chang | 1 | 133 | 6.75 |
| John W. Fisher III | 2 | 878 | 74.44 |
| Fisher III, John W. | 3 | 6 | 0.50 |