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 embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Learning to Generate Posters of Scientific Papers
Authors: Yuting Qiang, Yanwei Fu, Yanwen Guo, Zhi-Hua Zhou, Leonid Sigal
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Qualitative and quantitative results indicate the effectiveness of our approach. |
| Researcher Affiliation | Collaboration | Yuting Qiang1, Yanwei Fu2, Yanwen Guo1 , Zhi-Hua Zhou1 and Leonid Sigal2 1 National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China 2 Disney Research Pittsburgh, 4720 Frobes Avenue, Lower Level, 15213, USA |
| Pseudocode | Yes | The whole algorithm is summarized in Algorithm 1. (followed by a pseudocode block titled "Algorithm 1 Panel layout generation") |
| Open Source Code | No | The paper states: "We collect and make available a Poster-Paper dataset", referring to the dataset, but does not provide concrete access to the source code for the methodology described in the paper. |
| Open Datasets | Yes | We collect and make available to the community the ο¬rst Poster-Paper dataset. |
| Dataset Splits | Yes | We make a training and testing split: 20 pairs for training and ο¬ve for testing. There is total of 173 panels in our dataset. 143 for training and 30 for testing. |
| Hardware Specification | Yes | Our experiments were done on a PC with an Intel Xeon 2.0 GHz CPU and 144GB RAM. |
| Software Dependencies | No | The paper mentions "We use the Bayesian Network Toolbox (BNT) (Murphy 2002)" but does not specify a version number for the BNT software itself. |
| Experiment Setup | No | The paper does not explicitly provide details about the experimental setup such as specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or system-level training settings. |