Probabilistic Dependency Graphs
Authors: Oliver Richardson, Joseph Y Halpern12174-12181
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We introduce Probabilistic Dependency Graphs (PDGs), a new class of directed graphical models... We provide three semantics for PDGs, each of which can be derived from a scoring function... We show further that factor graphs and their exponential families can also be faithfully represented as PDGs, while there are significant barriers to modeling a PDG with a factor graph. |
| Researcher Affiliation | Academia | Oliver Richardson, Joseph Y. Halpern Cornell University, Computer Science Department, Ithaca NY 14853 {oli, halpern}@cs.cornell.edu |
| Pseudocode | No | The paper describes theoretical concepts, definitions, and theorems related to Probabilistic Dependency Graphs, but it does not include any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not contain any statements about releasing open-source code for the described methodology, nor does it provide any repository links. |
| Open Datasets | No | The paper is theoretical and does not conduct empirical studies using datasets. It provides illustrative examples but does not mention, provide access to, or cite any publicly available or open datasets. |
| Dataset Splits | No | The paper does not describe experimental validation on datasets, and therefore no information regarding training, validation, or test dataset splits is provided. |
| Hardware Specification | No | The paper is theoretical and does not report on experiments requiring specific hardware; thus, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe any experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper focuses on theoretical development and does not include details about an experimental setup, such as hyperparameters or system-level training settings. |