Convergence of Gradient EM on Multi-component Mixture of Gaussians
Authors: Bowei Yan, Mingzhang Yin, Purnamrita Sarkar
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we collect some numerical results. |
| Researcher Affiliation | Academia | Bowei Yan University of Texas at Austin boweiy@utexas.edu Mingzhang Yin University of Texas at Austin mzyin@utexas.edu Purnamrita Sarkar University of Texas at Austin purna.sarkar@austin.utexas.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., specific repository link, explicit code release statement) for its methodology. |
| Open Datasets | No | The paper mentions using 'N = 12, 000 data points' but does not specify a publicly available dataset by name, citation, or link. |
| Dataset Splits | No | The paper mentions 'N = 12,000 data points' but does not provide specific dataset split information (e.g., percentages, sample counts, or methodology for splitting). |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory) 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). |
| Experiment Setup | Yes | In all experiments we set the covariance matrix for each mixture component as identity matrix Id and define signal-to-noise ratio (SNR) as Rmin. ... For this set of experiments, we use a mixture of 3 Gaussians in 2 dimensions. In both experiments Rmax/Rmin = 1.5. In different settings of π, we apply gradient EM with varying SNR from 1 to 5. For each choice of SNR, we perform 10 independent trials with N = 12, 000 data points. |