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..
Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory
Authors: Ron Amit, Ron Meir
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we demonstrate the performance of our transfer method with image classification tasks solved by deep neural networks. In image classification, the data samples, z (x, y), consist of a an image, x, and a label, y. The hypothesis class hw : w Rd is the set of neural networks with a given architecture (which will be specified later). As a loss function ℓ(hw, z) we will use the cross-entropy loss. |
| Researcher Affiliation | Academia | 1The Viterbi Faculty of Electrical Engineering, Technion Israel Institute of Technology, Haifa, Israel. Correspondence to: Ron Amit <EMAIL>, Ron Meir <EMAIL>. |
| Pseudocode | Yes | Both algorithms are described in pseudo-code in the supplementary material (section A.4) 11 12. |
| Open Source Code | Yes | 11Code is available at: https://github.com/ ron-amit/meta-learning-adjusting-priors. |
| Open Datasets | Yes | We conduct two experiments with two different task environments, based on augmentations of the MNIST dataset (Le Cun, 1998). |
| Dataset Splits | No | The paper mentions 'meta-training set' and 'meta-test task' sizes but does not specify a separate validation set or its split information. |
| Hardware Specification | Yes | We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research. |
| Software Dependencies | No | The paper mentions using deep neural networks but does not provide specific software dependencies or their version numbers (e.g., Python, TensorFlow, PyTorch versions). |
| Experiment Setup | No | The paper states 'See section A.5 for more implementation details,' indicating that specific experimental setup details like hyperparameters are not present in the main text. |