Regularizing Flat Latent Variables with Hierarchical Structures
Authors: Rongcheng Lin, Huayu Li, Xiaojun Quan, Richang Hong, Zhiang Wu, Yong Ge
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results with two real world data sets and various evaluation metrics demonstrate the effectiveness of the proposed model. |
| Researcher Affiliation | Academia | UNC Charlotte. Email: {rlin4, hli38, yong.ge}@uncc.edu, Institute for Infocomm Research. Email: quanx@i2r.a-star.edu.sg Hefei University of Technology. Email: hongrc@hfut.edu.cn Nanjing University of Finance and Economics. Email: zawu@seu.edu.cn |
| Pseudocode | No | The paper describes the algorithm steps in paragraph form (e.g., 'the algorithm consists of 3 steps of modeling') but does not contain a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the proposed methodology is publicly available. |
| Open Datasets | Yes | Two real-world data sets are used to evaluate the performance of different algorithms: 20 Newsgroups1 and Encyclopedia Articles2. 1http://qwone.com/~jason/20Newsgroups/ 2http://www.cs.nyu.edu/~roweis/data.html |
| Dataset Splits | No | The paper specifies training and testing splits (e.g., '60% of documents' for training and '40% of documents' for testing), but does not explicitly mention a separate validation split for model training or hyperparameter tuning. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'software LIBLINEAR' and 'CLUTO' but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | As for MAP algorithms, including LDAMAP and STMMAP , the hyper parameters are fixed as α = 1.1 and β = 1.01 as many previous works did. And we set 200 EM iterations with random initialization for each model. As for HDP, we set up 10000 iterations to train the model and extra 1000 iterations to estimate the topic distribution. As for Variational algorithms, including LDAV B, STMV B and CTM, all models are fitted using initial hyper parameters α = 0.1 and β = 0.01 with random initialization. And we exhaustively run 100 variational EM iterations or until the relative change of lower bound being less than 10 5 to ensure the convergence. We find that 2-level STM is sufficient enough to model both data sets, thus in the following experiments the number of layers of stratified topic tree is fixed as LT = 2. Finally, the stratified topic tree is built by empirically setting the parameter λ as λ = 1.3. |