Tensor Variable Elimination for Plated Factor Graphs
Authors: Fritz Obermeyer, Eli Bingham, Martin Jankowiak, Neeraj Pradhan, Justin Chiu, Alexander Rush, Noah Goodman
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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. |