A central goal for AI and causality is thus the joint discovery of abstract representations and causal structure.
First, GNNs do not predispose interactions to be sparse, as relationships among independent entities are likely to be.
We explore the use of such a communication channel in the context of deep learning for modeling the structure of complex environments.
Models with a large number of parameters are prone to over-fitting and often fail to capture the underlying input distribution.
The rapid widespread of social media has lead to some undesirable consequences like the rapid increase of hateful content and offensive language.