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..
Equivariance Through Parameter-Sharing
Authors: Siamak Ravanbakhsh, Jeff Schneider, Barnabás Póczos
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The novelty of this work is its focus on the model symmetry as a gateway to equivariance. This gives us new theoretical guarantees for a strict notion of equivariance in neural networks. The core idea is simple: consider a colored bipartite graph Ωrepresenting a neural network layer. Edges of the same color represent tied parameters. This neural network layer as a function is equivariant to the actions of a given group G (and nothing more) iff the action of G is the symmetry group of Ω i.e., there is a simple bijection between parameter symmetries and equivariences of the corresponding neural network. The problem then boils down to designing colored bipartite graphs with given symmetries, which constitutes a major part of this paper. |
| Researcher Affiliation | Academia | 1School of Computer Science, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA, USA 15217. Correspondence to: Siamak Ravanbakhsh <EMAIL>. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not involve empirical experiments with datasets, so no dataset access information for training is provided. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments with datasets. Therefore, no dataset split information (e.g., training, validation, test splits) is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any empirical experiments or the hardware used to run them. |
| Software Dependencies | No | The paper is theoretical and does not describe any empirical experiments or software dependencies with specific version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any empirical experimental setup details, such as hyperparameters or training configurations. |