{"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/dataflow-matrix-machines-as-a-generalization","title":"Dataflow Matrix Machines as a Generalization of Recurrent Neural Networks","arxiv_id":"1603.09002","date":"2016-03-29","proceeding":null,"authors":["Michael Bukatin","Steve Matthews","Andrey Radul"],"abstract":"Dataflow matrix machines are a powerful generalization of recurrent neural\nnetworks. They work with multiple types of arbitrary linear streams, multiple\ntypes of powerful neurons, and allow to incorporate higher-order constructions.\nWe expect them to be useful in machine learning and probabilistic programming,\nand in the synthesis of dynamic systems and of deterministic and probabilistic\nprograms.","url_abs":"http://arxiv.org/abs/1603.09002v2","url_pdf":"http://arxiv.org/pdf/1603.09002v2.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":"dataflow-matrix-machines-as-a-generalization","repo_url":"https://github.com/anhinga/fluid","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"probabilistic-programming","task_name":"Probabilistic Programming"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}