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.
Beyond Shared Subspace: A View-Specific Fusion for Multi-View Multi-Label Learning
Authors: Gengyu Lyu, Xiang Deng, Yanan Wu, Songhe Feng7647-7654
AAAI 2022 | Venue PDF | LLM Run Details | Input Tokens: 18,973 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,425 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 have demonstrated that our proposed method significantly outperforms state-of-the-art methods. |
| Researcher Affiliation | Academia | Beijing Key Laboratory of Traffic Data Analysis and Mining School of Computer and Information Technology, Beijing Jiaotong University EMAIL |
| Pseudocode | Yes | Algorithm 1: The Training Process of D-VSM |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | Emotions1 http://mulan.sourceforge.net/datasets-mlc.html... Corel5k (Duygulu et al. 2002) and Espgame (Von Ahn and Dabbish 2004)... Pascal (Everingham et al. 2010) and Mirflickr (Huiskes and Lew 2008) |
| Dataset Splits | Yes | Finally, we conduct experimental comparison between our proposed D-VSM and all comparing methods, where five-fold cross-validation is performed on each data set. |
| Hardware Specification | No | The paper does not provide specific hardware details (GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not list specific software components with their version numbers. |
| Experiment Setup | Yes | In our experiments, we set k = 5. |