Visually Interpreting Names as Demographic Attributes by Exploiting Click-Through Data
Authors: Yan-Ying Chen, Yin-Hsi Kuo, Chun-Che Wu, Winston Hsu
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In the experiments, the automatic name-attribute associations can help gender inference with competitive accuracy by using manual labeling. It also benefits profiling social media users and keyword-based face image retrieval, especially for contributing 12% relative improvement of accuracy in adapting to unseen names. |
| Researcher Affiliation | Collaboration | National Taiwan University, Taipei, Taiwan FX Palo Alto Laboratory, Inc., California, USA |
| Pseudocode | No | The paper describes methods textually and through diagrams (Figure 2) but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code for the described methodology or a link to a code repository. |
| Open Datasets | Yes | In the experiments, we utilize a new large-scale real-world click data set publicly released by Microsoft Research and Bing (Hua et al. 2013). They sampled from one-year click logs of the Bing image search engine and formed the dataset Clickture-Lite, which contains 23 million click data with 1 million unique images. |
| Dataset Splits | Yes | Table 1 shows the accuracy reaches 75.76% in cross-validation manner (NAP-CV). |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models or memory specifications used for running its experiments. |
| Software Dependencies | No | The paper mentions software like 'SVM' and 'LIBSVM' (in references) but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | No | The paper states that 'The cost and gamma parameters are optimized by grid search' for SVM, but does not provide the specific values of these parameters or other detailed experimental setup information like learning rates, batch sizes, or training schedules. |