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..
Understanding Train-Validation Split in Meta-Learning with Neural Networks
Authors: Xinzhe Zuo, Zixiang Chen, Huaxiu Yao, Yuan Cao, Quanquan Gu
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our theory by conducting experiment on both synthetic and real datasets. |
| Researcher Affiliation | Academia | Department of Mathematics, University of California, Los Angles EMAIL Department of Computer Science, University of California, Los Angles EMAIL Department of Computer Science, Stanford University EMAIL Department of Statistics & Actuarial Science, University of Hong Kong EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access (link, explicit statement) to the source code for the methodology described. |
| Open Datasets | Yes | Real-world Data. In our experiments, we further justify our theoretical findings in two real-world datasets: Rainbow MNIST, mini Imagenet, which are discussed as follows. ...Following (Yao et al., 2021), Rainbow MNIST is a 10-way meta-learning dataset... Following the traditional meta-learning setting (Finn & Levine, 2017; Snell et al., 2017), mini Imagenet dataset is split into meta-training, meta-validation and meta-testing classes |
| Dataset Splits | Yes | mini Imagenet dataset is split into meta-training, meta-validation and meta-testing classes, where 64/16/20 classes are used for meta-training/validation/testing. We adopt the traditional Nway, K-shot setting to split the training and validation set in our experiment, where N=5 and K=1 in this paper (i.e., 5-way, 1-shot learning). |
| Hardware Specification | No | The paper does not specify any particular hardware (e.g., GPU/CPU models, memory details) used for running the experiments. |
| Software Dependencies | No | The paper mentions "Huberized-Re LU" as the activation function and "four-block convolutional layers as the base learner" but does not specify any software libraries or their version numbers. |
| Experiment Setup | Yes | Synthetic data. We generate synthetic data to test our theory. For our data generation we choose: d = 1000, K = 343, n = 10, σξ = 10.42, σs = 0.00066, ν 2 = 1. For our neural network we choose: m = 18, σ0 = 0.032. And finally we choose the following parameters for inner and outer level optimization: γ = 0.001, J = 5, η = 0.0001. ... The number of inner-loop steps is set as 5. The inner-loop and outer-loop learning rates are set as: 0.01 and 0.001 (mini Imagenet), 0.1 and 0.01 (Rainbow MNIST), respectively. |