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..
Anchor-Free Correlated Topic Modeling: Identifiability and Algorithm
Authors: Kejun Huang, Xiao Fu, Nikolaos D. Sidiropoulos
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments using the TDT2 and Reuters-21578 corpus demonstrate that the proposed anchor-free approach exhibits very favorable performance (measured using coherence, similarity count, and clustering accuracy metrics) compared to the prior art. |
| Researcher Affiliation | Academia | Kejun Huang Xiao Fu Nicholas D. Sidiropoulos Department of Electrical and Computer Engineering University of Minnesota Minneapolis, MN 55455, USA EMAIL EMAIL EMAIL |
| Pseudocode | Yes | Algorithm 1: Successive Projection Algorithm [6]... Algorithm 2: Anchor Free |
| Open Source Code | No | The paper describes its algorithms and experimental procedures but does not provide a link or explicit statement about making the source code for its method publicly available. |
| Open Datasets | Yes | In this section, we apply the proposed algorithm and the baselines to two popular text mining datasets, namely, the NIST Topic Detection and Tracking (TDT2) and the Reuters-21578 corpora. |
| Dataset Splits | No | The paper mentions using TDT2 and Reuters-21578 datasets for experiments but does not specify exact training, validation, or test split percentages, sample counts, or refer to predefined splits with citations for reproducibility of data partitioning. |
| Hardware Specification | No | The paper discusses runtime performance and computational complexity but does not specify the particular hardware (e.g., CPU, GPU models, or memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'well-established tools for eigen-decomposition' and 'the solver proposed in [18]' but does not provide specific version numbers for any software dependencies, libraries, or programming environments. |
| Experiment Setup | No | The paper describes data preprocessing steps (e.g., 'standard tf-idf data', 'normalized-cut weighted (NCW) [21] is applied') but does not provide specific experimental setup details such as hyperparameter values, optimizer settings, or explicit training configurations. |