{"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/neural-arithmetic-logic-units","title":"Neural Arithmetic Logic Units","arxiv_id":"1808.00508","date":"2018-08-01","proceeding":"NeurIPS 2018 12","authors":["Andrew Trask","Felix Hill","Scott Reed","Jack Rae","Chris Dyer","Phil Blunsom"],"abstract":"Neural networks can learn to represent and manipulate numerical information,\nbut they seldom generalize well outside of the range of numerical values\nencountered during training. To encourage more systematic numerical\nextrapolation, we propose an architecture that represents numerical quantities\nas linear activations which are manipulated using primitive arithmetic\noperators, controlled by learned gates. We call this module a neural arithmetic\nlogic unit (NALU), by analogy to the arithmetic logic unit in traditional\nprocessors. Experiments show that NALU-enhanced neural networks can learn to\ntrack time, perform arithmetic over images of numbers, translate numerical\nlanguage into real-valued scalars, execute computer code, and count objects in\nimages. 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