How does identifying symmetries improve my machine learning models?

Building symmetries into your model architecture, rather than forcing the model to learn them, leads to significantly better sample efficiency, faster convergence, and more robust generalization.

What is the difference between invariance and equivariance in this tool?

Invariance refers to outputs that remain unchanged when input is transformed (like classification labels), while equivariance refers to outputs that transform predictably alongside the input (like bounding boxes or segmentation masks).

Do I need to know group theory to use this skill?

No, the skill is designed to guide you through domain-specific questions that translate your observations about data behavior into formal symmetry groups automatically.

What data types are supported by the questionnaire?

The skill includes specific modules for images (2D), 3D data (point clouds/meshes), molecular structures, graphs, and temporal data like time series.

Symmetry Discovery Questionnaire

Name: Symmetry Discovery Questionnaire
Author: lyndonkl

bylyndonkl

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数据科学与机器学习

Guides machine learning engineers through identifying data symmetries and physical constraints to optimize model architectures.

关于

This skill provides a structured framework for machine learning practitioners to uncover hidden symmetries in their datasets, such as rotation, translation, and reflection invariance. By walking users through a collaborative discovery process involving domain classification, coordinate analysis, and transformation testing, it helps determine whether specific model components should be invariant or equivariant. This process ensures better sample efficiency and improved generalization by aligning model architecture with the underlying geometric and physical properties of the data, even for users without a background in formal group theory.

主要功能

8 GitHub stars
Structured 6-step workflow for systematic symmetry identification
Automated mapping of data properties to formal mathematical groups like SO(3) and SE(3)
Domain-specific transformation tests for images, 3D data, graphs, and molecules
Physical constraint analysis based on conservation laws and Noether's theorem
Standardized output templates for documenting invariance and equivariance candidates

使用场景

Designing equivariant neural networks for medical imaging or 3D point clouds
Analyzing scientific datasets to ensure models respect physical conservation laws
Determining if graph-based models require permutation invariance for node labeling

关于

主要功能

8 GitHub stars
Structured 6-step workflow for systematic symmetry identification
Automated mapping of data properties to formal mathematical groups like SO(3) and SE(3)
Domain-specific transformation tests for images, 3D data, graphs, and molecules
Physical constraint analysis based on conservation laws and Noether's theorem
Standardized output templates for documenting invariance and equivariance candidates

使用场景

Designing equivariant neural networks for medical imaging or 3D point clouds
Analyzing scientific datasets to ensure models respect physical conservation laws
Determining if graph-based models require permutation invariance for node labeling