Instance-Level Label Propagation with Multi-Instance Learning
Authors: Qifan Wang, Gal Chechik, Chen Sun, Bin Shen
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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 {wqfcr, gal, chensun, bshen}@google.com |
| 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. |