Semi-supervised Max-margin Topic Model with Manifold Posterior Regularization
Authors: Wenbo Hu, Jun Zhu, Hang Su, Jingwei Zhuo, Bo Zhang
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that such tight coupling brings significant benefits in quantitative and qualitative performance. We now present the empirical results of our semi-supervised topic model. |
| Researcher Affiliation | Academia | Wenbo Hu, Jun Zhu, Hang Su, Jingwei Zhuo, Bo Zhang Tsinghua National Laboratory for Information Science and Technology (TNList), State Key Lab for Intelligent Technology and Systems, Center for Brain-Inspired Computing Research (CBICR), Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China {hwb13@mails., dcszj@, suhangss@, zhuojw10@mails., dcszb@}tsinghua.edu.cn |
| Pseudocode | No | The paper provides detailed mathematical formulations and descriptions of the proposed method, but it does not include a clearly labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | Yes | We consider a binary document set which consists of two subgroups of the 20Newsgroups data2, alt.atheism and talk.religion.misc. This sub-dataset consists of 856 training documents and 569 testing documents. ... 2http://qwone.com/ jason/20Newsgroups |
| Dataset Splits | Yes | The parameter c2 is the regularization parameter for the manifold regularization which is chosen from {0.1, 0.01, 0.001} via 5-fold cross validation. This sub-dataset consists of 856 training documents and 569 testing documents. |
| Hardware Specification | No | The paper discusses training time and efficiency of the models but does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Ada Grad stepsize [Duchi et al., 2011]' and 'SGLD steps' as part of the optimization process but does not list specific software libraries or their version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | For LDA-based models, we set α = 1, β = 1 and topic number K = 20. For Med LDA-based models, we set ℓ= 164 and c1 = 1. The parameter c2 is the regularization parameter for the manifold regularization which is chosen from {0.1, 0.01, 0.001} via 5-fold cross validation. The expectation of the topic assignments Z are calculated with 5 samples and for graph construction, we set the nearest neighbor number as 10 for 20Newsgroups dataset and 5 for Yahoo news dataset. ... For the stochastic gradient MCMC, the stepsizes for classifier weights η are Ada Grad stepsize [Duchi et al., 2011] and stepsizes for topic-word parameter Φ are set as 10 (1 + t/100) 0.6 at iteration t. |