Scalable and Interpretable Data Representation for High-Dimensional, Complex Data
Authors: Been Kim, Kayur Patel, Afshin Rostamizadeh, Julie Shah
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We quantitatively and qualitatively evaluate an efficient, accurate and scalable feature-compression method using latent Dirichlet allocation for discrete data. ... Through user study, we measure human performance according to both objective and subjective terms: accuracy, efficiency and user confidence and preference. |
| Researcher Affiliation | Collaboration | Massachusetts Institute of Technology, Google |
| Pseudocode | No | The paper does not contain any sections or figures explicitly labeled 'Pseudocode' or 'Algorithm', nor are there structured steps formatted like code. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or provide a link to a code repository for the methodology described. |
| Open Datasets | Yes | We used a dataset of news articles (20 Newsgroups (Lang 1995)) |
| Dataset Splits | No | The paper mentions a '20% test set and train on the remaining 80%' but does not explicitly provide details about a validation set or its split. |
| Hardware Specification | No | The paper does not provide specific details on the hardware used for running experiments, such as CPU or GPU models, or memory. |
| Software Dependencies | No | The paper mentions 'Gibbs Sampling implementation' and cites 'Phan and Nguyen 2013' but does not specify version numbers for any software dependencies. |
| Experiment Setup | Yes | The parameter α is set to 0.1, and β is set to 0.1. ... a vector of length K (set to 4 in our experiment). |