Weakly-Supervised Multi-view Multi-instance Multi-label Learning
Authors: Yuying Xing, Guoxian Yu, Jun Wang, Carlotta Domeniconi, Xiangliang Zhang
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results show the effectiveness of WSM3L on benchmark datasets. and section titles 4 Experiments, 4.1 Experimental Setup, 4.2 Results on Completely Paired Multi-view Data, etc. |
| Researcher Affiliation | Academia | 1College of Computer and Information Sciences, Southwest University, Chongqing, China 2CEMSE, King Abdullah University of Science and Technology, Thuwal, SA 3Department of Computer Science, George Mason University, VA, USA |
| Pseudocode | No | We give the optimization procedure as a supplementary file. The main paper does not contain pseudocode or an algorithm block. |
| Open Source Code | No | We give the optimization procedure as a supplementary file. and These results are given in the supplementary file(mlda.swu.edu.cn/WSM3L). This does not explicitly state that the source code for the methodology is provided, nor is the link a direct link to a code repository. |
| Open Datasets | Yes | We collect eight publicly available multi-instance multi-label datasets and one real M3 dataset from different domains for the experiments. The details of these datasets are listed in Table 1. The first four datasets1 and Isoform dataset [Yu et al., 2020] are used to evaluate the predicted labels of bags, since baglevel labels are available one. The last four datasets have instance-level labels for evaluation [Briggs et al., 2012]. and footnote 1http://lamda.nju.edu.cn/CH.Data.ashx |
| Dataset Splits | No | We randomly select 70% of the bags of a dataset to train the model, and use the remaining 30% for testing. There is no explicit mention of a validation split. |
| Hardware Specification | No | The paper does not provide any specific hardware details (like GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | The input parameters of WSM3L are set as follows: d = 160, s = 150, k = 30 and α = 1. ... We further investigated the sensitivity of four input parameters (i.e., α, k, s and d). |