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..
Scalable Bayesian Meta-Learning through Generalized Implicit Gradients
Authors: Yilang Zhang, Bingcong Li, Shijian Gao, Georgios B. Giannakis
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive numerical tests are also carried out to empirically validate the performance of the proposed method. |
| Researcher Affiliation | Academia | Dept. of ECE, University of Minnesota, Minneapolis, MN, USA EMAIL |
| Pseudocode | Yes | Algorithm 1: Implicit Bayesian meta-learning (i Ba ML) |
| Open Source Code | Yes | Our implementation relies on the Py Torch (Paszke et al. 2019), and codes are available at https://github.com/zhangyilang/i Ba ML. |
| Open Datasets | Yes | We consider one of the most widely used few-shot dataset for classification mini Image Net (Vinyals et al. 2016). |
| Dataset Splits | Yes | We adopt the dataset splitting suggested by (Ravi and Larochelle 2017), where 64, 16 and 20 disjoint classes are used for meta-training, meta-validation and meta-testing, respectively. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models, memory, or cloud instances) used for experiments were mentioned. |
| Software Dependencies | No | Our implementation relies on the Py Torch (Paszke et al. 2019). |
| Experiment Setup | Yes | The model is a standard 4-layer 32-channel convolutional neural network, and the chosen baseline algorithms are MAML (Finn, Abbeel, and Levine 2017) and ABML (Ravi and Beatson 2019); see also the Appendix for alternative setups. Due to the large number of training tasks, it is impractical to compute the exact meta-training loss. As an alternative, we adopt the test nll (averaged over 1, 000 test tasks) as our metric, and also report their corresponding accuracy. For fairness, we set L = 5 when implementing the implicit gradients so that the time complexity is similar to explicit one with K = 5. |