Closed-form Marginal Likelihood in Gamma-Poisson Matrix Factorization
Authors: Louis Filstroff, Alberto Lumbreras, Cédric Févotte
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6. Experimental results We now compare the three MCEM algorithms proposed for MMLE in the Ga P model, first using synthetic toy datasets, then real-world data. |
| Researcher Affiliation | Academia | 1IRIT, Universit e de Toulouse, CNRS, France. |
| Pseudocode | No | The paper describes the steps of the MCEM algorithm but does not present them in a clearly labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | Yes | Python implementations of the three algorithms are available from the first author website. |
| Open Datasets | Yes | Finally, we consider the NIPS dataset which contains word counts from a collection of articles published at the NIPS conference.1 The number of articles is N = 1, 500 and the number of unique terms (appearing at least 10 times after tokenization and removing stop-words) is F = 12, 419. 1https://archive.ics.uci.edu/ml/datasets/bag+of+words |
| Dataset Splits | No | The paper does not specify explicit train/validation/test dataset splits. For synthetic data, it states N=100 samples, and for NIPS, N=1,500 articles and F=12,419 terms without mentioning how these were partitioned for training, validation, or testing. |
| Hardware Specification | No | The paper mentions 'CPUtime' but does not provide any specific hardware details such as GPU models, CPU models, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Python implementations' but does not specify any version numbers for Python or any specific software libraries or dependencies used. |
| Experiment Setup | Yes | We proceed to estimate the dictionary W using hyperparemeters K = K + 1 = 3, αk = βk = 1 with MCEM-C, MCEM-H and MCEM-CH. The algorithms are run for 500 iterations. 300 Gibbs samples are generated at each iteration, with the first 150 samples being discarded for burn-in (this proves to be enough in practice), leading to J = 150. The Gibbs sampler at EM iteration i + 1 is initialized with the last sample obtained at EM iteration i (warm restart). The algorithms are initialized from the same deterministic starting point given by Wfk = 1, as suggested by Equation (49). |