4 accepted papers
In the field of equivariant networks, achieving affine equivariance, particularly for general group representations, has long been a challenge. In this paper, we propose the steerable EquivarLayer, a generalization of InvarLayer (Li et al., 2024), by building on the concept of equivariants beyond in…
Equivariant networks enhance model efficiency and generalization by embedding symmetry priors into their architectures. However, most existing methods, primarily based on group convolutions and steerable convolutions, face significant limitations when dealing with complex transformation groups, part…
The Max-$k$-Cut problem is a fundamental combinatorial optimization challenge that generalizes the classic $\mathcal{NP}$-complete Max-Cut problem. While relaxation techniques are commonly employed to tackle Max-$k$-Cut, they often lack guarantees of equivalence between the solutions of the original…
Convolutional neural networks benefit from translation equivariance achieving tremendous success. Equivariant networks further extend this property to other transformation groups. However most existing methods require discretization or sampling of groups leading to increased model sizes for larger g…