Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Dirichlet belief networks for topic structure learning
Authors: He Zhao, Lan Du, Wray Buntine, Mingyuan Zhou
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on text corpora demonstrate the advantages of the proposed model. |
| Researcher Affiliation | Academia | 1Faculty of Information Technology, Monash University, Australia 2Mc Combs School of Business, The University of Texas at Austin, USA |
| Pseudocode | Yes | Omitted details of inference as well as the overall algorithm are given in the supplementary materials. |
| Open Source Code | Yes | Code available at https://github.com/ethanhezhao/Dir BN |
| Open Datasets | Yes | The experiments were conducted on three real-world datasets, detailed as follows: 1) Web Snippets (WS), containing 12,237 web search snippets labelled with 8 categories... 2) Tag My News (TMN), consisting of 32,597 RSS news labelled with 7 categories... 3) Twitter, extracted in 2011 and 2012 microblog tracks at Text REtrieval Conference (TREC)5. It has 11,109 tweets in total... Footnote 5: http://trec.nist.gov/data/microblog.html |
| Dataset Splits | Yes | To compute perplexity, we randomly selected 80% of the documents in each dataset to train the models and 20% for testing. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU/CPU models, memory specifications). |
| Software Dependencies | No | The paper mentions “Mallet” but does not provide specific version numbers for any software, libraries, or programming languages used for implementation or experimentation. |
| Experiment Setup | Yes | For all the models, we ran 3,000 MCMC iterations with 1,500 burnin. For Dir BN, we set a0 = b0 = g0 = h0 = 1.0 and e0 = f0 = 0.01... For all the models, the number of topics in each layer of Dir BN was set to 100, i.e., KT = = K1 = 100. |