Deep Poisson Factor Modeling
Authors: Ricardo Henao, Zhe Gan, James Lu, Lawrence Carin
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present experiments on three corpora: 20 Newsgroups (20 News), Reuters corpus volume I (RCV1) and Wikipedia (Wiki). |
| Researcher Affiliation | Academia | Department of Electrical and Computer Engineering Duke University, Durham, NC 27708 {r.henao,zhe.gan,james.lu,lcarin}@duke.edu |
| Pseudocode | No | The paper describes the inference procedures verbally and with mathematical equations but does not include structured pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The code used, implemented in Matlab, will be made publicly available. |
| Open Datasets | Yes | We present experiments on three corpora: 20 Newsgroups (20 News), Reuters corpus volume I (RCV1) and Wikipedia (Wiki). |
| Dataset Splits | No | The paper specifies training and test sets for 20 News (11,315 training, 7,531 test) and describes how held-out data is used for perplexity calculation, but it does not explicitly state a distinct validation set split for hyperparameter tuning. |
| Hardware Specification | No | The paper discusses computational complexity and runtimes for the models but does not provide specific hardware details such as GPU or CPU models, or cloud computing specifications, used for running the experiments. |
| Software Dependencies | No | The paper states the code is 'implemented in Matlab' but does not provide specific version numbers for Matlab or any other ancillary software dependencies used for the experiments. |
| Experiment Setup | Yes | For our model, we run 3,000 samples (first 2,000 as burnin) for MCMC and 4,000 iterations with 200-document mini-batches for SVI. and In the experiments, we set κ = 0.7 and τ = 128. and In the experiments, we run 100 Gibbs sampling cycles per layer. |