Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Tensor Variable Elimination for Plated Factor Graphs
Authors: Fritz Obermeyer, Eli Bingham, Martin Jankowiak, Neeraj Pradhan, Justin Chiu, Alexander Rush, Noah Goodman
ICML 2019 | Venue PDF | LLM Run Details | Input Tokens: 18,026 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,532 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experiment with plated factor graphs as a modeling language for three tasks: polyphonic music prediction, animal movement modeling and latent sentiment analysis. We report our results in Table 1. |
| Researcher Affiliation | Collaboration | 1Uber AI Labs 2Harvard University 3Stanford University. |
| Pseudocode | Yes | Algorithm 1 TENSORVARIABLEELIMINATION |
| Open Source Code | Yes | Open-source implementations are available; see http://docs.pyro.ai/en/dev/ops.html |
| Open Datasets | Yes | Dataset Model JSB Piano Nottingham |
| Dataset Splits | No | The paper refers to datasets like JSB Piano, Nottingham, and Sentihood, but does not specify how these datasets were split into training, validation, and test sets (e.g., percentages or sample counts). |
| Hardware Specification | Yes | Figure 5 shows results obtained on an Nvidia Quadro P6000 GPU. |
| Software Dependencies | No | The paper mentions 'Pyro probabilistic programming language' and 'Num Py', but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | No | The paper describes model variants and overall experimental design (e.g., '12 different latent variable models'), but it does not provide specific hyperparameter values (e.g., learning rate, batch size, epochs) or detailed training configurations in the main text. It defers some details to supplementary materials. |