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.
Learning Robust Representations for Data Analytics
Authors: Sheng Li
IJCAI 2016 | Venue PDF | LLM Run Details | Input Tokens: 8,673 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,312 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 image datasets show promising results on clustering and semisupervised classification [Li and Fu, 2015a]. These methods have obtained remarkable improvements on many real-world applications, such as image classification [Li and Fu, 2015a], human motion segmentation [Li et al., 2015a], person re-identification [Li et al., 2015b], etc.. Experimental results on three action and gesture datasets show that TSC outperforms the related methods, which validates the effectiveness of robust dictionary learning [Li et al., 2015a]. |
| Researcher Affiliation | Academia | Sheng Li Advisor: Yun Fu Northeastern University, Boston, MA, USA |
| Pseudocode | No | The paper describes algorithms (e.g., "Non-convex optimization algorithms are designed"), but it does not include pseudocode or algorithm blocks formatted as such. |
| Open Source Code | No | The paper does not provide any specific link or statement regarding the release of its source code. |
| Open Datasets | Yes | These methods have obtained remarkable improvements on many real-world applications, such as image classification [Li and Fu, 2015a], human motion segmentation [Li et al., 2015a], person re-identification [Li et al., 2015b], etc.. Experimental results on three action and gesture datasets show that TSC outperforms the related methods, which validates the effectiveness of robust dictionary learning [Li et al., 2015a]. |
| Dataset Splits | No | The paper mentions experiments on datasets but does not provide specific details on training, validation, or test dataset splits (e.g., percentages, sample counts, or cross-validation setup). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers for reproducibility. |
| Experiment Setup | No | The paper provides an objective function but does not include specific experimental setup details such as hyperparameter values (e.g., learning rate, batch size, number of epochs) or detailed training configurations. |