Markov Mixed Membership Models
Authors: Aonan Zhang, John Paisley
ICML 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results demonstrate that Markov M3 performs well compared with tree structured topic models, and can learn meaningful dependency structure between topics.In Section 4 we present experiments on two large grouped discrete data sets. |
| Researcher Affiliation | Academia | Aonan Zhang AZ2385@COLUMBIA.EDU John Paisley JPAISLEY@COLUMBIA.EDU Department of Electrical Engineering, Columbia University, New York, NY, USA |
| Pseudocode | Yes | Algorithm 1 Generative process for Markov M3; Algorithm 2 An outline of batch variational inference |
| Open Source Code | No | The paper does not provide any explicit statements about open-sourcing the code for the methodology described, nor does it include any repository links. |
| Open Datasets | Yes | We also experiment with Million Song Dataset (Bertin Mahieux et al., 2011). |
| Dataset Splits | Yes | We split each data set into a training set and a test set, also shown in Table 1. Table 1. Three datasets used for batch comparison. Corpus # train # test # vocab # tokens Huff Post 3.5K 589 6,313 907K Science 4K 1K 4,403 1.39M Nips 2.2K 300 14,086 3.3M |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers for replication. |
| Experiment Setup | Yes | For Markov M3, we truncate the path length to 15 and set γ0 = 1. We also use a learning rate of (10 + t) 0.75 for both models, and a batch size of |Ct| = 500. |