Robust Spectral Inference for Joint Stochastic Matrix Factorization
Authors: Moontae Lee, David Bindel, David Mimno
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Validating on both real and synthetic data, we demonstrate that our rectification not only produces better clusters, but also, unlike previous work, learns meaningful cluster interactions. |
| Researcher Affiliation | Academia | Moontae Lee, David Bindel Dept. of Computer Science Cornell University Ithaca, NY 14850 {moontae,bindel}@cs.cornell.edu David Mimno Dept. of Information Science Cornell University Ithaca, NY 14850 mimno@cornell.edu |
| Pseudocode | No | The paper describes the algorithm steps in narrative text (e.g., 'In this section, we describe how to estimate...'), but does not include formal pseudocode blocks or algorithm figures. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing their source code, nor does it provide a link to a code repository for their proposed method. |
| Open Datasets | Yes | We use two text datasets: NIPS full papers and New York Times news articles.8 ... users movie reviews from the Movielens 10M Dataset,9 and music playlists from the complete Yes.com dataset.10 |
| Dataset Splits | No | The paper mentions using 'M training examples' and evaluating metrics, but it does not specify explicit train/validation/test dataset splits (e.g., percentages or sample counts) for their experiments. |
| Hardware Specification | No | The paper does not specify the hardware used for running experiments, such as specific GPU or CPU models, or memory configurations. |
| Software Dependencies | No | The paper mentions using 'Mallet with the standard option' for Gibbs sampling but does not provide specific version numbers for Mallet or any other software dependencies. |
| Experiment Setup | Yes | We run DC 30 times for each experiment, randomly permuting the order of objects and using the median results to minimize the effect of different orderings. We also run 150 iterations of AP alternating PSDNK, NORN, and NN N in turn. For probabilistic Gibbs sampling, we use the Mallet with the standard option doing 1,000 iterations. |