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.
One Positive Label is Sufficient: Single-Positive Multi-Label Learning with Label Enhancement
Authors: Ning Xu, Congyu Qiao, Jiaqi Lv, Xin Geng, Min-Ling Zhang
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 20,106 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,060 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 | Experiments on twelve corrupted MLL datasets show the effectiveness of SMILE over several existing SPMLL approaches. |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, Southeast University, Nanjing 210096, China 2 RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 SMILE Algorithm |
| Open Source Code | Yes | Source code is available at https://github.com/palm-ml/smile. |
| Open Datasets | Yes | In the experiments, we adopt twelve widely-used MLL datasets [13], which cover a broad range of cases with diversi๏ฌed multi-label properties. |
| Dataset Splits | Yes | For each dataset, we run the comparing methods with 80%/10%/10% train/validation/test split. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or specific computational resources) used for running experiments. |
| Software Dependencies | No | The paper mentions using 'Adam optimizer [17]' but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | The mini-batch size and the number of epochs are set to 16 and 25, respectively. The learning rate and weight decay are selected from {10 4, 10 3, 10 2} with a validation set. |