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Found 3 papers, 1 papers with code
Directed Graph Embeddings in Pseudo-Riemannian Manifolds
The inductive biases of graph representation learning algorithms are often encoded in the background geometry of their embedding space.
Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairness
Learning meaningful representations of data that can address challenges such as batch effect correction and counterfactual inference is a central problem in many domains including computational biology.
The tractability of CSP classes defined by forbidden patterns
We show that this framework generalises relational properties and allows us to capture known hybrid tractable classes.
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