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..
Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace
Authors: Yoonho Lee, Seungjin Choi
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We performed experiments to answer: Do our novel components (TW, M etc) improve metalearning performance? (6.1) Is applying a mask M row-wise actually better than applying one parameter-wise? (6.1) To what degree does T alleviate the need for careful tuning of step size α? (6.2) In MT-nets, does learned subspace dimension reflect the difficulty of tasks? (6.3) Can T-nets and MT-nets scale to large-scale metalearning problems? (6.4) |
| Researcher Affiliation | Academia | 1Department of Computer Science and Engineering, Pohang University of Science and Technology, Korea. |
| Pseudocode | Yes | Algorithm 1 Transformation Networks (T-net); Algorithm 2 Mask Transformation Networks (MT-net) |
| Open Source Code | No | The paper mentions 'Most of our experiments were performed by modifying the code accompanying (Finn et al., 2017)', but it does not provide a link or explicit statement about the availability of their own source code. |
| Open Datasets | Yes | To compare the performance of MT-nets to prior work in meta-learning, we evaluate our method on few-shot classification on the Omniglot (Lake et al., 2015) and Mini Imagenet (Ravi & Larochelle, 2017) datasets. |
| Dataset Splits | No | The paper describes training and testing examples per task ('Each task consists of K {5, 10, 20} training examples and 10 testing examples') but does not explicitly mention a distinct validation dataset split with specific percentages or counts. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU models, CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'Adam (Kingma & Ba, 2015)' as a meta-optimizer, but it does not specify software components or libraries with version numbers. |
| Experiment Setup | Yes | We used Adam (Kingma & Ba, 2015) as our meta-optimizer with a learning rate of β = 10 3. Taskspecifc learners used step size α = 10 2. We initialize all ζ to 0, all T as identity matrices, and all W as truncated normal matrices with standard deviation 10 2. |