1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2021★ 1 cited
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.…
cs.LG2021
Intrinsic uncertainties and where to find them
Francesco Farina, Lawrence Phillips, Nicola J Richmond
We introduce a framework for uncertainty estimation that both describes and extends many existing methods. We consider typical hyperparameters involved in classical training as ran…
cs.LG2021
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…