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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.…
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…
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…
NCoRE: Neural Counterfactual Representation Learning for Combinations of Treatments
Sonali Parbhoo, Stefan Bauer, Patrick Schwab
Estimating an individual's potential response to interventions from observational data is of high practical relevance for many domains, such as healthcare, public policy or economi…