Learning to Generate Posters of Scientific Papers
Authors: Yuting Qiang, Yanwei Fu, Yanwen Guo, Zhi-Hua Zhou, Leonid Sigal
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | 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 first Poster-Paper dataset. |
| Dataset Splits | Yes | We make a training and testing split: 20 pairs for training and five 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. |