Spectral Methods for Supervised Topic Models
Authors: Yining Wang, Jun Zhu
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Thorough experiments on a diverse range of synthetic and real-world datasets verify the theory and demonstrate the practical effectiveness of the algorithm. |
| Researcher Affiliation | Academia | Machine Learning Department, Carnegie Mellon University, yiningwa@cs.cmu.edu Dept. of Comp. Sci. & Tech.; Tsinghua National TNList Lab; State Key Lab of Intell. Tech. & Sys., Tsinghua University, dcszj@mail.tsinghua.edu.cn |
| Pseudocode | Yes | Algorithm 1 spectral parameter recovery algorithm for s LDA. |
| Open Source Code | No | The paper states, 'Both Gibbs sampling for the s LDA model in Fig. 1 (b) and the proposed spectral recovery algorithm are implemented in C++,' but it does not provide any link or explicit statement about the availability of the source code. |
| Open Datasets | Yes | For real-world data, we use the large-scale dataset built on Amazon movie reviews [16] to demonstrate the practical effectiveness of our algorithm. |
| Dataset Splits | Yes | Error bars indicate the standard deviation of 5-fold cross-validation. |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU/CPU models or memory used for running the experiments. |
| Software Dependencies | No | The paper states that the algorithms are 'implemented in C++' but does not provide specific version numbers for C++ compilers or any other libraries/software dependencies. |
| Experiment Setup | Yes | For our spectral algorithm, the hyperparameters L and T are set to 100, which is sufficiently large for all settings in our experiments. The hyper-parameters used in our Gibbs sampling implementation are the same with the ones used to generate the datasets. |