Learning With Incomplete Labels
Authors: Yingming Li, Zenglin Xu, Zhongfei Zhang
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive evaluations on three benchmark datasets demonstrate that ICVL and ICVL-OD outstand with superior performances in comparison with the competing methods. |
| Researcher Affiliation | Academia | College of Information Science & Electronic Engineering, Zhejiang University, China School of Computer Science and Engineering, University of Electronic Science and Technology of China |
| Pseudocode | Yes | Algorithm 1: ICVL Algorithm |
| Open Source Code | No | The paper does not provide an explicit statement about releasing the source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | All datasets are obtained from http://mulan.sourceforge.net/datasets-mlc.html. |
| Dataset Splits | Yes | On the Enron and Birds datasets, we follow the experimental setup used in Mulan. Since there is no fixed split in the Bookmarks dataset in Mulan, we use a fixed training set of 80% of the data, and evaluate the performance of our predictions on the fixed test set of 20% of the data. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper describes algorithms and optimization methods but does not list specific software dependencies with version numbers (e.g., programming languages, libraries, or solvers). |
| Experiment Setup | No | The paper mentions regularization parameters and a label dropout probability but does not provide specific values for these or other hyperparameters (e.g., learning rate, batch size, epochs) or detailed training configurations in the main text. |