{"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/relational-neural-expectation-maximization","title":"Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions","arxiv_id":"1802.10353","date":"2018-02-28","proceeding":"ICLR 2018 1","authors":["Sjoerd van Steenkiste","Michael Chang","Klaus Greff","Jürgen Schmidhuber"],"abstract":"Common-sense physical reasoning is an essential ingredient for any\nintelligent agent operating in the real-world. For example, it can be used to\nsimulate the environment, or to infer the state of parts of the world that are\ncurrently unobserved. In order to match real-world conditions this causal\nknowledge must be learned without access to supervised data. To address this\nproblem we present a novel method that learns to discover objects and model\ntheir physical interactions from raw visual images in a purely\n\\emph{unsupervised} fashion. It incorporates prior knowledge about the\ncompositional nature of human perception to factor interactions between\nobject-pairs and learn efficiently. On videos of bouncing balls we show the\nsuperior modelling capabilities of our method compared to other unsupervised\nneural approaches that do not incorporate such prior knowledge. We demonstrate\nits ability to handle occlusion and show that it can extrapolate learned\nknowledge to scenes with different numbers of objects.","url_abs":"http://arxiv.org/abs/1802.10353v1","url_pdf":"http://arxiv.org/pdf/1802.10353v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"relational-neural-expectation-maximization","repo_url":"https://github.com/sjoerdvansteenkiste/Relational-NEM","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"relational-neural-expectation-maximization","repo_url":"https://github.com/BorealisAI/PROVIDE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"relational-neural-expectation-maximization","repo_url":"https://github.com/BorealisAI/Spatio-Temporal","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"common-sense-reasoning","task_name":"Common Sense Reasoning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1802.10353","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.10353"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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