Variational Inference for Sequential Distance Dependent Chinese Restaurant Process
Authors: Sergey Bartunov, Dmitry Vetrov
ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5. EXPERIMENTS In this section we empirically compare our variational algorithms with a set of baselines. We start from comparison with Gibbs sampler as it is currently the only available inference algorithm for dd CRP. Next we compare our variational inference for CRP-equivalent mixture model and variational inference for Dirichlet Process. Then we evaluate distance-dependent mixture model against CRP mixture. We release our software implementation used for the experiments. |
| Researcher Affiliation | Academia | Sergey Bartunov SBOS@SBOS.IN Dorodnicyn Computing Centre of the Russian Academy of Sciences, Moscow RUSSIA Dmitry P. Vetrov VETROVD@YANDEX.RU Moscow State University, Moscow RUSSIA Higher School of Economics, Moscow RUSSIA |
| Pseudocode | Yes | Algorithm 1 Variational inference for seqdd CRP |
| Open Source Code | Yes | We release our software implementation used for the experiments. http://github.com/sbos/seqddcrp.jl |
| Open Datasets | No | Our dataset consisted from 2246 news articles from the Associated Press, we performed word stemming, but did not remove stop-words. (No citation, link, or specific source for this AP dataset is provided.) |
| Dataset Splits | No | The paper only mentions a train/test split ("Each dataset was split into 200 train data points and 200 test data points.") but does not explicitly describe a validation split. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for the experiments. |
| Software Dependencies | No | The paper mentions its implementation is in Julia (implied by the GitHub link `seqddcrp.jl`), but it does not specify the Julia version or any other software dependencies with version numbers. |
| Experiment Setup | Yes | We set parameter = 0.1 for both models to encourage small number of clusters and provided weak informative priors for covariance matrix to slightly suggest it s spherical form. Truncation level for DP VB was set to 50. |