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..
Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels
Authors: Massimiliano Patacchiola, Jack Turner, Elliot J. Crowley, Michael O'Boyle, Amos J. Storkey
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically demonstrate that DKT outperforms several state-of-the-art algorithms in few-shot classification, and is the state of the art for cross-domain adaptation and regression. |
| Researcher Affiliation | Academia | Massimiliano Patacchiola School of Informatics University of Edinburgh EMAIL Jack Turner School of Informatics University of Edinburgh EMAIL Elliot J. Crowley School of Engineering University of Edinburgh EMAIL Michael O Boyle School of Informatics University of Edinburgh EMAIL Amos Storkey School of Informatics University of Edinburgh EMAIL |
| Pseudocode | Yes | Algorithm 1 Deep Kernel Transfer (DKT) in the few-shot setting, train and test functions. |
| Open Source Code | Yes | We derive two versions of DKT for both the regression and the classification setting, comparing it against recent methods on a standardized benchmark environment; the code is released with an open-source license1. 1https://github.com/Bayes Watch/deep-kernel-transfer |
| Open Datasets | Yes | We consider two challenging datasets: the Caltech-UCSD Birds (CUB-200, Wah et al., 2011), and mini-Image Net (Ravi and Larochelle, 2017). |
| Dataset Splits | Yes | In the most common scenario, training, validation and test datasets each consist of distinct tasks sampled from the same overall distribution over tasks. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments, only discussing the software framework used. |
| Software Dependencies | No | The paper mentions 'Py Torch and GPy Torch' but does not provide specific version numbers for these or any other software components, which are necessary for reproducibility. |
| Experiment Setup | Yes | The training set is composed of 5 support and 5 query points, and the test set of 5 support and 200 query points. All the experiments are 5-way (5 random classes) with 1 or 5-shot (1 or 5 samples per class in the support set). A total of 16 samples per class are provided for the query set. |