#separation-power News & Analysis

Separation Power of Equivariant Neural Networks

Researchers characterize the separation power of equivariant neural networks, demonstrating that non-polynomial activations like ReLU and sigmoid achieve equivalent maximum expressivity, while depth and architectural choices significantly influence a model's ability to distinguish inputs. This theoretical analysis provides a framework for comparing model expressivity and understanding the design principles behind convolutional and permutation-invariant networks.