Integrating Bayesian and Discriminative Sparse Kernel Machines for Multi-class Active Learning
Authors: Weishi Shi, Qi Yu
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments conducted over both synthetic and real data and comparison with competitive AL methods demonstrate the effectiveness of the proposed model. |
| Researcher Affiliation | Academia | Weishi Shi Rochester Institute of Technology ws7586@rit.edu Qi Yu Rochester Institute of Technology qi.yu@rit.edu |
| Pseudocode | No | The paper describes its methods using mathematical equations and prose, but it does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The details are summarized in the supplementary materials and the source code is available at [21]. Weishi Shi and Qi Yu. Source Code and Data. https://drive.google.com/drive/folders/1kk50i_Dvg_R8Pdp_B8lbt32rqn9KGTDFB4n?usp=sharing, 2019. [Online; accessed 12-October-2019]. |
| Open Datasets | Yes | We choose 6 datasets from different domains, as summarized in Table 1, to evaluate the proposed KMC based multi-class sampling model. Table 1: Description of Datasets Dataset #Inst #Attr #Classes Class Distr. Domain Yeast 1484 8 10 Skewed Biology Reuters 10788 5227 75 Skewed News Penstroke 1144 500 26 Even Image Derm 1 800 1391 50 Even Medical Derm 2 868 1554 30 Even Medical Auto-drive 58509 48 11 Even Automobile. Weishi Shi and Qi Yu. Source Code and Data. https://drive.google.com/drive/folders/1kk50i_Dvg_R8Pdp_B8lbt32rqn9KGTDFB4n?usp=sharing, 2019. [Online; accessed 12-October-2019]. |
| Dataset Splits | No | The paper mentions using 70% of synthetic data for training and 30% for testing, but it does not specify a separate validation split or explicit training/validation/test splits for the real datasets. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware specifications (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | Yes | Thus, the problem can be solved much more efficiently by quadratic solvers boosted by sparse input such as MOSEK [20]. MOSEK Ap S. MOSEK Optimizer API for Python 8.1.0.80, 2019. |
| Experiment Setup | Yes | For KMC, unless otherwise specified, parameter C is set to 10 2 and the convergence threshold is set to 10 3. Two important parameters of KMC, the large margin coefficient C and the initial sample size S, are set to 1 and 40 respectively when compared with other AL models. |