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..
Hierarchically Structured Meta-learning
Authors: Huaxiu Yao, Ying Wei, Junzhou Huang, Zhenhui Li
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results show that our approach can achieve state-of-the-art performance in both toy-regression and few-shot image classification problems. |
| Researcher Affiliation | Collaboration | 1College of Information Science and Technology, Pennsylvania State University, PA, USA 2Tencent AI Lab, Shenzhen, China. |
| Pseudocode | Yes | Algorithm 1 Meta-training of HSML |
| Open Source Code | No | The paper does not provide any explicit statements about open-sourcing code or links to a code repository. |
| Open Datasets | Yes | Caltech-UCSD Birds-200-2011 (Bird) (Wah et al., 2011), Describable Textures Dataset (Texture) (Cimpoi et al., 2014), Fine-Grained Visual Classification of Aircraft (Aircraft) (Maji et al., 2013), and FGVCx-Fungi (Fungi) (Fun, 2018) |
| Dataset Splits | Yes | Similar to the preprocessing of Mini Imagenet (Vinyals et al., 2016), we divide each dataset to meta-training, meta-validation and meta-testing classes. |
| Hardware Specification | No | The paper does not provide specific hardware details such as CPU/GPU models or cloud configurations used for experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper states 'We specify the hyperparameters for meta-training in supplementary material C.' but does not include these specific details in the main text. |