{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-simple-neural-network-module-for-relational","title":"A simple neural network module for relational reasoning","arxiv_id":"1706.01427","date":"2017-06-05","proceeding":"NeurIPS 2017 12","authors":["Adam Santoro","David Raposo","David G. T. Barrett","Mateusz Malinowski","Razvan Pascanu","Peter Battaglia","Timothy Lillicrap"],"abstract":"Relational reasoning is a central component of generally intelligent\nbehavior, but has proven difficult for neural networks to learn. In this paper\nwe describe how to use Relation Networks (RNs) as a simple plug-and-play module\nto solve problems that fundamentally hinge on relational reasoning. We tested\nRN-augmented networks on three tasks: visual question answering using a\nchallenging dataset called CLEVR, on which we achieve state-of-the-art,\nsuper-human performance; text-based question answering using the bAbI suite of\ntasks; and complex reasoning about dynamic physical systems. Then, using a\ncurated dataset called Sort-of-CLEVR we show that powerful convolutional\nnetworks do not have a general capacity to solve relational questions, but can\ngain this capacity when augmented with RNs. 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