Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Universal Equivariant Multilayer Perceptrons
Authors: Siamak Ravanbakhsh
ICML 2020 | Venue PDF | LLM Run Details | Input Tokens: 19,593 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,783 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper proves the universality of a broad class of equivariant MLPs with a single hidden layer. In particular, it is shown that having a hidden layer on which the group acts regularly is sufficient for universal equivariance (invariance). For example, some types of steerable-CNNs become universal. Another corollary is the unconditional universality of equivariant MLPs for all Abelian groups. A third corollary is the universality of equivariant MLPs with a high-order hidden layer, where we give both group-agnostic bounds and groupspecific bounds on the order of the hidden layer that guarantees universal equivariance. |
| Researcher Affiliation | Academia | 1School of Computer Science, Mc Gill University, Montreal Canada. 2Mila Quebec AI Institute.. Correspondence to: Siamak Ravanbakhsh <EMAIL>. |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not use or reference any datasets for empirical evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not specify any hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup. |