research
∙
06/16/2021
Directed Graph Embeddings in Pseudo-Riemannian Manifolds
The inductive biases of graph representation learning algorithms are oft...
share
research
∙
06/15/2021
Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairness
Learning meaningful representations of data that can address challenges ...
research
∙
12/01/2018
Interpretable Graph Convolutional Neural Networks for Inference on Noisy Knowledge Graphs
In this work, we provide a new formulation for Graph Convolutional Neura...
research
∙
03/08/2011