Relational inductive biases, deep learning, and graph networks
Relational inductive biases, deep learning, and graph networks Battaglia et al., arXiv’18
Earlier this week we saw the argument that causal reasoning (where most of the interesting questions lie!) requires more than just associational machine learning. Structural causal models have at their core a graph of entities and relationships between them. Today we’ll be looking at a position paper with a wide team of authors from DeepMind, Google Brain, MIT, and the University of Edinburgh, which also makes the case for graph networks as a foundational building block of the next generation of AI. In other words, bringing back and re-integrating some of the techniques from the AI toolbox that were prevalent when resources were more limited.
We argue that combinatorial generalization must be a top priority for AI to achieve human-like abilities, and that structured representation 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 the rules for composing them.
Relational reasoning and structured approaches
Human’s represent complex systems as compositions of entities and their interactions. We use hierarchies to abstract away fine-grained differences, manage part-whole associations and other more Continue reading
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