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..
Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural Processes
Authors: Peter Holderrieth, Michael J Hutchinson, Yee Whye Teh
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In experiments with Gaussian process vector fields, images, and real-world weather data, we observe that Steer CNPs significantly improve the performance of previous models and equivariance leads to improvements in transfer learning tasks. |
| Researcher Affiliation | Collaboration | 1University of Oxford, United Kingdom 2Deep Mind, United Kingdom. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide a specific repository link or an explicit statement about the release of source code for the methodology described. |
| Open Datasets | Yes | MNIST and rot MNIST. We first train models on completion tasks from the MNIST data set (Le Cun et al., 2010). |
| Dataset Splits | No | The paper mentions splitting the dataset into "train, validation and test data set" but does not provide specific percentages, sample counts, or a detailed splitting methodology within the provided text. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions software tools like Pytorch, Num Py, Sci Py, and Matplotlib, but does not provide specific version numbers for these ancillary software components. |
| Experiment Setup | No | The paper describes the general training process including minimization of log-likelihood by gradient descent, but it does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs, optimizer settings) in the main text. |