Equivariance Through Parameter-Sharing
Authors: Siamak Ravanbakhsh, Jeff Schneider, Barnabás Póczos
ICML 2017 | Conference PDF | Archive PDF | Plain Text | 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 <mravanba@cs.cmu.edu>. |
| 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. |