Teaching-to-Learn and Learning-to-Teach for Multi-label Propagation
Authors: Chen Gong, Dacheng Tao, Jie Yang, Wei Liu
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Thorough empirical studies show that due to the optimized propagation sequence designed by the teachers, ML-TLLT yields generally better performance than seven state-of-the-art methods on the typical multi-label benchmark datasets. This section first validates several critical steps in the proposed ML-TLLT, and then compares ML-TLLT with seven state-of-the-art methods on five benchmark datasets. |
| Researcher Affiliation | Collaboration | Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University Centre for Quantum Computation and Intelligent Systems, University of Technology Sydney Didi Research, Beijing, China |
| Pseudocode | Yes | Algorithm 1 The curvilinear search for minimizing (7) and Algorithm 2 PALM for solving S(r)-subproblem (6) |
| Open Source Code | No | The paper does not provide an explicit statement about the release of source code for the described methodology, nor does it include a link to a code repository. |
| Open Datasets | Yes | All the adopted datasets come from the MULAN repository. 1http://mulan.sourceforge.net/datasets-mlc.html |
| Dataset Splits | Yes | The reported results of various algorithms on all the datasets are produced by 5-fold cross validation. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | In ML-TLLT, the trade-off parameters β0 and β1 are set to 1 for all the experiments. As suggested by (Chen et al. 2008), we set u = 1, v = 0.15 in SMSE-HF, and β = γ = 1 in SMSE-LGC. The weighting parameter C in MLSVM (Linear) and MLSVM (RBF) is tuned to 1. |