First-Order Model Counting in a Nutshell
Authors: Guy Van den Broeck
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We give an overview of model counting as it is applied in statistical relational learning, probabilistic programming, databases, and hybrid reasoning. A short tutorial illustrates the principles behind these solvers. |
| Researcher Affiliation | Academia | Guy Van den Broeck Computer Science Department University of California, Los Angeles guyvdb@cs.ucla.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. It provides examples of logical formulas and discusses principles, but no algorithmic steps are presented in a formal pseudocode format. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described within this paper. It is an overview and tutorial paper rather than one introducing new code. |
| Open Datasets | No | The paper is a theoretical overview and tutorial and does not involve training models on datasets. Therefore, it does not provide information about public datasets used for training. |
| Dataset Splits | No | This paper is a theoretical overview and tutorial and does not present experiments that would require training, validation, or test dataset splits. Therefore, no such information is provided. |
| Hardware Specification | No | The paper is a theoretical overview and tutorial and does not describe any experiments that would require hardware specifications. Thus, no such details are provided. |
| Software Dependencies | No | The paper is a theoretical overview and tutorial and does not describe any specific experimental setup or implementation details that would require listing software dependencies with version numbers. Thus, no such information is provided. |
| Experiment Setup | No | The paper is a theoretical overview and tutorial and does not describe any experiments or their setup. Therefore, no details regarding hyperparameter values, training configurations, or system-level settings are provided. |