The Semi-random Likelihood of Doctrinal Paradoxes
Authors: Ao Liu, Lirong Xia5124-5132
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on synthetic data in the next Section confirm our theoretical results. We conduct numerical experiments to verify the results in Theorem 1. |
| Researcher Affiliation | Academia | Ao Liu and Lirong Xia Department of Computer Science, Rensselaer Polytechnic Institute 110 8th St, Troy, NY 12180, USA liua6@rpi.edu, xial@cs.rpi.edu |
| Pseudocode | No | The paper discusses proof techniques but does not include any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper mentions "synthetic data" but does not provide any specific access information (link, DOI, citation) for a publicly available dataset used for training. |
| Dataset Splits | No | The paper mentions conducting "numerical experiments" on "synthetic data" but does not provide specific details about training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, processor types, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | No | The paper mentions "numerical experiments" but does not provide specific experimental setup details such as hyperparameter values, training configurations, or system-level settings. |