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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Online Structured Meta-learning
Authors: Huaxiu Yao, Yingbo Zhou, Mehrdad Mahdavi, Zhenhui (Jessie) Li, Richard Socher, Caiming Xiong
NeurIPS 2020 | Venue PDF | LLM Run Details | Input Tokens: 18,242 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,278 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on three datasets demonstrate the effectiveness and interpretability of our proposed framework in the context of both homogeneous and heterogeneous tasks. |
| Researcher Affiliation | Collaboration | 1Pennsylvania State University, 2Salesforce Research |
| Pseudocode | Yes | Algorithm 1 Online Meta-learning Pipeline of OSML |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the described methodology. |
| Open Datasets | Yes | Here, we follow [10] and create a Rainbow MNIST dataset, which contains a sequence of tasks generated from the original MNIST dataset. ... The first dataset is generated from mini-Imagenet. ... We create the second dataset called Meta-dataset by following [37, 41]. |
| Dataset Splits | No | The paper does not explicitly provide training/test/validation dataset splits. It mentions support and query sets within tasks, and a test set for evaluation, but no dedicated validation split. |
| Hardware Specification | No | No specific hardware details (like CPU/GPU models or memory) are provided for the experimental setup. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | Require: β1, β2, β3,β4, β5: learning rates ... We report hyperparameters and model structures in Appendix A.2. |