Relational inductive biases, deep learning, and graph networks

Peter W., Battaglia,; Jessica B., Hamrick,; Victor, Bapst,; Sánchez‐González, Álvaro; Zambaldi, Vinícius; Malinowski, Mateusz; Tacchetti, Andrea; Raposo, David; Santoro, Adam; Faulkner, Ryan; Gülçehre, Çağlar; Francis Song, H.; J. Ballard, Andrew; Gilmer, Justin; E. Dahl, George; Vaswani, Ashish; R. Allen, Kelsey; Nash, C.; Langston, Victoria; Dyer, Chris; Heess, Nicolas; Wierstra, Daan; Kohli, Pushmeet; Botvinick, Matthew; Vinyals, Oriol; Li, Yujia; Pascanu, Razvan

doi:10.4230/lipics.cp.2025.27

Public

Relational inductive biases, deep learning, and graph networks

Published in arXiv (Cornell University) • Jun 4, 2018

Authors:

Battaglia, Peter W.

Hamrick, Jessica B.

Bapst, Victor

Abstract

Artificial intelligence (AI) has undergone a renaissance recently, making major progress in key domains such as vision, language, control, and decision-making. This has been due, in part, to cheap data and cheap compute resources, which have fit the natural strengths of deep learning. However, many ...

Subject

Inductive bias

Statistical relational learning

Computer science

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