equivariant-architecture-designer

What problem does it solve?

This Skill helps you design neural network architectures that inherently respect the symmetries of your data, leading to more efficient, robust, and generalizable models.

Core Features & Use Cases

• Symmetry-Aware Architecture Design: Recommends network structures tailored to specific symmetry groups (e.g., rotations, translations, permutations).
• Library and Layer Guidance: Suggests appropriate libraries (like e3nn, escnn) and equivariant layer types (convolutions, nonlinearities, normalization).
• Use Case: When building a model for 3D molecular data where rotational and translational symmetry is crucial, this Skill will guide you in selecting E(3)-equivariant layers and designing a network topology that leverages these symmetries for better performance.

Quick Start

Use the equivariant-architecture-designer skill to design a neural network architecture for E(3) symmetry on point cloud data.