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..
Effective Meta-Regularization by Kernelized Proximal Regularization
Authors: Weisen Jiang, James Kwok, Yu Zhang
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results con๏ฌrm the advantage of the proposed algorithm. |
| Researcher Affiliation | Academia | 1 Department of Computer Science and Engineering, Southern University of Science and Technology 2 Department of Computer Science and Engineering, Hong Kong University of Science and Technology 3 Peng Cheng Laboratory |
| Pseudocode | Yes | Algorithm 1 MAML [12]. Algorithm 2 Common Mean [7]. Algorithm 3 Meta Prox. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. It mentions using 'CVXPYLayers package [1]' but does not state that their own implementation code is released. |
| Open Datasets | Yes | Data sets. Experiments are performed on three data sets. (i) Sine. ... (ii) Sale. This is a real-world dataset from [36] ... (iii) QMUL, which is a multiview face dataset [16] from Queen Mary University of London. ... We use the standard 5-way K-shot setting (K = 1 or 5) on the mini-Image Net [39] dataset, which consists of 100 randomly chosen classes from ILSVRC-2012 [33]. |
| Dataset Splits | Yes | We randomly generate a meta-training set of 8000 tasks, a meta-validation set of 1000 tasks for early stopping, and a meta-testing set of 2000 tasks for performance evaluation. ... We randomly split the tasks into a meta-training set of 600 tasks, a meta-validation set of 100 tasks, and a meta-testing set of 111 tasks. ... the 100 classes are randomly split into 64 for meta-training, 16 for meta-validation, and 20 for meta-testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Adam optimizer [20]' and 'CVXPYLayers package [1]' but does not provide specific version numbers for these software dependencies to ensure reproducibility. |
| Experiment Setup | Yes | We use the Adam optimizer [20] with a learning rate of 0.001. Each minibatch has 16 tasks. For Sine and Sale, the model (ฯ and fฮธ) is meta-trained for 40, 000 iterations. ... We train the model for 80, 000 iterations, and each mini-batch has 4 tasks. We use the Adam optimizer [20] with an initial learning rate of 0.001, which is then reduced by half every 2, 500 iterations. |