Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Online Metric Learning for Multi-Label Classification
Authors: Xiuwen Gong, Dong Yuan, Wei Bao4012-4019
AAAI 2020 | Venue PDF | LLM Run Details | Input Tokens: 14,805 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,536 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments on a number of benchmark multi-label datasets validate our theoretical approach and illustrate that our proposed online metric learning (OML) algorithm outperforms state-of-the-art methods. |
| Researcher Affiliation | Academia | Xiuwen Gong, Dong Yuan, Wei Bao Faculty of Engineering, The University of Sydney EMAIL |
| Pseudocode | Yes | Algorithm 1 Online Metric Learning for Multi-Label Classification |
| Open Source Code | No | The paper states 'The codes are provided by the respective authors.' in the context of baseline methods, and provides a link to a dataset source, but there is no explicit statement or link for the open-source code of their own proposed methodology. |
| Open Datasets | Yes | To evaluate the performance of our proposed online metric learning algorithm, we conduct experiments on four benchmark datasets: Corel5k, Enron, Medical and Emotions. The statistics of these datasets can be found in website1. 1http://mulan.sourceforge.net |
| Dataset Splits | No | The paper states 'Initially, we keep 20% of data for nearest neighbor searching.' but does not provide specific train/validation/test dataset splits needed to reproduce the experiment. |
| Hardware Specification | Yes | All experiments are conducted on a workstation with 3.20GHz Intel CPU and 16GB main memory, running the Windows 10 platform. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | In our experiment, the matrix V1 is initialized as a normal distributed random matrix. Initially, we keep 20% of data for nearest neighbor searching. In our experiment, M is set to 100000 and m is set to 0.00001, while k is set to 10. Parameter λ in OLANSGD is chosen from among {10 6, 10 5, , 100} using five-fold cross validation. We use the default parameter for OSML-ELM. |