Name
Abstract | ||
|---|---|---|
Topic models, such as LDA and its variants, are popular probabilistic models for discovering the abstract “topics” that occur in a collection of documents. However, the performance of topic models may vary a lot for different workloads, and it is not a trivial task to achieve a well-optimized implementation. In this paper, we systematically study all recently proposed samplers over LDA: AliasLDA, ... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TKDE.2019.2956518 | IEEE Transactions on Knowledge and Data Engineering |
Keywords | DocType | Volume |
Inference algorithms,Data models,Probabilistic logic,Cultural differences,Computational modeling,Complexity theory,Resource management | Journal | 33 |
Issue | ISSN | Citations |
6 | 1041-4347 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |