An Instability in Variational Inference for Topic Models
Authors: Behrooz Ghorbani, Hamid Javadi, Andrea Montanari
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We carried out extensive numerical simulations with the naive mean field iteration. ... In Figure 1 we plot empirical results for the average V(c W ), V(c H) for k = 2, ν = 1 and four values of δ, within the Gaussian model. In Figure 2 (left frame), we plot the empirical probability that variational inference does not converge to the uninformative fixed point or, more precisely, b P(V(c W ) ε0) with ε0 = 10 4, evaluated on a grid of (β, δ) values, for the same model. |
| Researcher Affiliation | Academia | 1Department of Electrical Engineering, Stanford University, CA 2Digital Signal Processing Group, Rice University, TX 3Department of Statistics, Stanford University, CA. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks (clearly labeled algorithm sections or code-like formatted procedures). |
| Open Source Code | No | The paper does not include an unambiguous statement that the authors are releasing the source code for the work described, nor does it provide a direct link to a source-code repository. |
| Open Datasets | No | We select a two-dimensional grid of (δ, β) s and generate 400 different instances according to the LDA model for each grid point. The paper uses synthetic data generated according to a model, not a publicly available dataset, and thus provides no concrete access information to a public dataset. |
| Dataset Splits | No | The paper does not explicitly provide training/test/validation dataset splits (e.g., exact percentages, sample counts, or citations to predefined splits) needed to reproduce the data partitioning. It uses synthetic data generated from a model for '400 random realizations'. |
| Hardware Specification | No | The paper mentions 'numerical simulations' but does not provide any specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | We initialize the naive mean field iteration near the uninformative fixed-point and iterate until a convergence criterion or the maximum number of 300 iterations is reached. |