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..
Scalars are universal: Equivariant machine learning, structured like classical physics
Authors: Soledad Villar, David W Hogg, Kate Storey-Fisher, Weichi Yao, Ben Blum-Smith
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We complement our theory with numerical examples that show that the scalar-based method is simple, ef๏ฌcient, and scalable. ... We present numerical experiments using our scalar-based approach compared to other methods in Section 7 (see also [96]). |
| Researcher Affiliation | Academia | Soledad Villar Department of Applied Mathematics and Statistics Johns Hopkins University David W. Hogg Flatiron Institute a divison of the Simons Foundation Kate Storey-Fisher Center for Cosmology and Particle Physics Department of Physics, New York University Weichi Yao Department of Technology, Operations, and Statistics Stern School of Business, New York University Ben Blum-Smith Center for Data Science New York University |
| Pseudocode | No | The paper does not include any pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is available on Git Hub2, and it reuses much of the functionality provided by EMLP [28]. |
| Open Datasets | Yes | We demonstrate our approach using scalar-based multi-layer perceptrons (MLP) on two toy learning tasks from [28]: an O(5)-invariant task and an O(3)-equivariant task. |
| Dataset Splits | No | The paper mentions 'Test error as a function of training set size' but does not specify the splits for training, validation, or test sets in percentages or absolute counts, nor does it explicitly mention a validation set. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper mentions that the code reuses functionality from EMLP [28] and uses MLPs, but it does not specify software names with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | No | The paper describes the tasks and models used (MLPs), but it does not provide specific experimental setup details such as learning rates, batch sizes, number of epochs, or optimizer settings. |