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.
Instance-Level Label Propagation with Multi-Instance Learning
Authors: Qifan Wang, Gal Chechik, Chen Sun, Bin Shen
IJCAI 2017 | Venue PDF | LLM Run Details | Input Tokens: 16,110 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,694 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 | Experimental results on two benchmark datasets demonstrate the effectiveness of the proposed approach over several state-of-the-art methods. |
| Researcher Affiliation | Industry | Qifan Wang, Gal Chechik, Chen Sun and Bin Shen Google Research Mountain View, CA 94043, US EMAIL |
| Pseudocode | Yes | Algorithm 1 Instance-Level Label Propagation with Multi Instance Learning (ILLP) |
| Open Source Code | No | The paper provides a link for the MISSL baseline method's code ('The code is available from http://www.cs.cmu.edu/ juny/MILL/') but does not state that the code for the proposed ILLP method is open-source or provide a link for it. |
| Open Datasets | Yes | The proposed ILLP approach is evaluated with three configurations of experiments on two benchmarks: an image dataset SIVAL2 and a text corpus Reuters (Reuters21578)3. ... SIVAL2 (http://www.cs.wustl.edu/ sg/multi-inst-data/) ... Reuters3 (http://www.daviddlewis.com/resources/testcollections/) |
| Dataset Splits | Yes | In each experiment, we randomly partition the examples in each category into two splits to form the labeled and unlabeled sets. The trade-off parameters α and β are tuned using five-fold cross-validation. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running experiments are mentioned in the paper. |
| Software Dependencies | No | The paper mentions 'tf-idf features' and refers to existing implementations for baseline parameters, but it does not specify any software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x, scikit-learn x.x). |
| Experiment Setup | Yes | The trade-off parameters α and β are tuned using five-fold cross-validation. We set the number of neighbors k to 8 to construct the k-NN graph. |