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 [1].
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. |