Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Robust Self-Supervised Multi-Instance Learning with Structure Awareness
Authors: Yejiang Wang, Yuhai Zhao, Zhengkui Wang, Meixia Wang
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Compared with state-of-the-art supervised MIL baselines, SMILES achieves average improvement of 4.9%, 4.4% in classification accuracy on 5 benchmark datasets and 20 newsgroups datasets, respectively. In addition, we show that the model is robust to the input corruption. |
| Researcher Affiliation | Academia | Yejiang Wang1, Yuhai Zhao1, Zhengkui Wang2, Meixia Wang1 1School of Computer Science and Engineering, Northeastern University, China 2Info Comm Technology Cluster, Singapore Institute of Technology, Singapore EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: SMILES. 1: input: unlabeled training data X X, batch size N, temperature τ, augmentation ratio c, encoder network frep, pre-train head network g. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of the described methodology. |
| Open Datasets | Yes | We empirically evaluate SMILES against state-of-the-art supervised multi-instance learning algorithms on five popular benchmark datasets, twenty text datasets from the 20Newsgroups corpus and three datasets for the task of biocreative text categorization (see the Appendix for detail). |
| Dataset Splits | Yes | For SMILES, we report the mean 10-fold cross validation accuracy after 5 runs followed by a linear SVM. The linear SVM is trained by applying cross validation on training data folds and the best mean accuracy is reported. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions software components like 'linear SVM' and implicitly 'neural networks', but it does not specify any version numbers for these or other software dependencies. |
| Experiment Setup | Yes | We conduct experiment with the values of the number of message passing layers, the number of epochs, batch size, the parameter C of SVM, the threshold ϵ, augmentation ratio c and temperature τ in the sets {2, 4, 8, 12}, {10, 20, 40, 100}, {32, 64, 128, 256}, {10 3, . . . , 102, 103}, {0.1, . . . , 0.5}, {10%, . . . , 50%} and {0.05, 0.1, 0.2, 0.5, 1.0, 2.0} respectively. The hidden dimension of layer is set to 128. |