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We argue that combinatorial generalization must be a top priority for AI to achieve human-like abilities, and that structured representations and computations are key to realizing this objective.
We explore how using relational inductive biases within deep learning architectures can facilitate learning about entities, relations, and rules for composing them.
We present a new building block for the AI toolkit with a strong relational inductive bias--the graph network--which generalizes and extends various approaches for neural networks that operate on graphs, and provides a straightforward interface for manipulating structured knowledge and producing structured behaviors.
In particular, generalizing beyond one's experiences--a hallmark of human intelligence from infancy--remains a formidable challenge for modern AI.
The following is part position paper, part review, and part unification.
Commonly, these attributes are distributed unequally in the population.
For example, in many schools across the United States and Europe, Asian or Black students form a minority.
In recent years, models have been proposed that consider homophily.
We build on these models by systematically exploring the parameter range for homophily and group size differences and offer analytical and empirical evidence on the emergent properties of networks and the ranking of groups.
Homophily can put minority groups at a disadvantage by restricting their ability to establish links with a majority group or to access novel information.
Here, we show how this phenomenon can influence the ranking of minorities in examples of real-world networks with various levels of heterophily and homophily ranging from sexual contacts, dating contacts, scientific collaborations, and scientific citations.