210 citations · 218 across the 7 of their papers we have counts for
4 papers · 1 filter
GNisi: A graph network for reconstructing Ising models from multivariate binarized data
Emma Slade, Sonya Kiselgof, Lena Granovsky +1
Ising models are a simple generative approach to describing interacting binary variables. They have proven useful in a number of biological settings because they enable one to repr…
Data efficiency in graph networks through equivariance
Francesco Farina, Emma Slade
We introduce a novel architecture for graph networks which is equivariant to any transformation in the coordinate embeddings that preserves the distance between neighbouring nodes.…
Symmetry-driven graph neural networks
Francesco Farina, Emma Slade
Exploiting symmetries and invariance in data is a powerful, yet not fully exploited, way to achieve better generalisation with more efficiency. In this paper, we introduce two grap…
Beyond permutation equivariance in graph networks
Emma Slade, Francesco Farina
In this draft paper, we introduce a novel architecture for graph networks which is equivariant to the Euclidean group in -dimensions. The model is designed to work with graph ne…