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.
Attention-Induced Embedding Imputation for Incomplete Multi-View Partial Multi-Label Classification
Authors: Chengliang Liu, Jinlong Jia, Jie Wen, Yabo Liu, Xiaoling Luo, Chao Huang, Yong Xu
AAAI 2024 | Venue PDF | LLM Run Details | Input Tokens: 20,061 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,647 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 | extensive experiments on five datasets confirm the advancement and effectiveness of our embedding imputation method and multi-view multi-label classification model. |
| Researcher Affiliation | Academia | 1School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 2College of Computer Science and Software Engineering, Shenzhen University 3School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University |
| Pseudocode | Yes | Algorithm 1: Training process of AIMNet |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the described methodology. |
| Open Datasets | Yes | Datasets: In line with prior research (Tan et al. 2018; Li and Chen 2022; Liu et al. 2023a), we select five widely recognized multi-view multi-label datasets for our experiments, i.e., Corel5k, Pascal07, ESPGame, IAPRTC12, and MIRFLICKR. |
| Dataset Splits | No | The paper states 'we randomly select 70% of all data as the training set' but does not explicitly define a separate validation set or its size. |
| Hardware Specification | No | The paper does not specify any particular hardware (e.g., GPU model, CPU type) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software names with version numbers (e.g., specific libraries or frameworks like PyTorch 1.9). |
| Experiment Setup | No | The paper mentions 'hyperparameters (τ, learning rate, and training epochs E)' in Algorithm 1 but does not provide their specific values in the main text. |