Self-Paced Active Learning: Query the Right Thing at the Right Time
Authors: Ying-Peng Tang, Sheng-Jun Huang5117-5124
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that the proposed approach is superior to state-of-the-art batch mode active learning methods. |
| Researcher Affiliation | Academia | College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 211106, China {tangyp, huangsj}@nuaa.edu.cn |
| Pseudocode | Yes | Algorithm 1 The SPAL Algorithm |
| Open Source Code | No | The paper mentions using external tools like CVX and MOSEK, providing their URLs: 'CVX (Grant and Boyd 2014) and MOSEK 1 are used to solve the QP problem. 1http://www.mosek.com/ http://cvxr.com/cvx'. However, there is no explicit statement about releasing the source code for their own proposed method (SPAL) or a link to its repository. |
| Open Datasets | Yes | We perform the experiments on 9 datasets, whose sizes are summarized in Table 1. Dataset thyroid antivirus clean1 # Instances 215 373 476 Dataset tictactoe image krvskp # Instances 958 2086 3196 Dataset phoneme gisette phishing # Instances 5404 7000 11055 |
| Dataset Splits | Yes | For each dataset, we randomly sample 40% instances as the test set, and the rest 60% instances for the training. Further, 5% of the training set is used as the initially labeled data, while the rest instances consist of the unlabeled pool for active selection. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory specifications, or cloud computing instance types) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'CVX (Grant and Boyd 2014) and MOSEK 1 are used to solve the QP problem' but does not specify their version numbers or other software dependencies with specific versions. |
| Experiment Setup | Yes | For the proposed method SPAL, we fix µ = 0.1, and γ = 0.1. For the SPL parameter λ, we initialize it with a certain value which is selected from {0.1, 0.01}, and follow the method used in (Lin et al. 2018) to update it linearly with a small fixed value. In our experiments, we fix λpace = 0.01 for all datasets. Specifically, we have the following updating rule for λ at tth iteration: λt = λinitial + (t - 1) λpace. We fix batch size b = 5 for all methods. The parameters of BMDR are set to the recommended values in their paper. Specifically, the regularized weight γ = 0.1 and the trade-off parameter µ = 1000. |