{"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/associative-long-short-term-memory","title":"Associative Long Short-Term Memory","arxiv_id":"1602.03032","date":"2016-02-09","proceeding":null,"authors":["Ivo Danihelka","Greg Wayne","Benigno Uria","Nal Kalchbrenner","Alex Graves"],"abstract":"We investigate a new method to augment recurrent neural networks with extra\nmemory without increasing the number of network parameters. The system has an\nassociative memory based on complex-valued vectors and is closely related to\nHolographic Reduced Representations and Long Short-Term Memory networks.\nHolographic Reduced Representations have limited capacity: as they store more\ninformation, each retrieval becomes noisier due to interference. Our system in\ncontrast creates redundant copies of stored information, which enables\nretrieval with reduced noise. Experiments demonstrate faster learning on\nmultiple memorization tasks.","url_abs":"http://arxiv.org/abs/1602.03032v2","url_pdf":"http://arxiv.org/pdf/1602.03032v2.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":"associative-long-short-term-memory","repo_url":"https://github.com/henrysteinitz/holographic-reduced-representations","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"associative-long-short-term-memory","repo_url":"https://github.com/henrysteinitz/neural-memory","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"associative-long-short-term-memory","repo_url":"https://github.com/mohammadpz/Associative_LSTM","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"memorization","task_name":"Memorization"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[{"method_slug":"adam","method_name":"Adam"},{"method_slug":"associative-lstm","method_name":"Associative LSTM"},{"method_slug":"holographic-reduced-representation","method_name":"Holographic Reduced Representation"},{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[{"slug":"associative-lstm","name":"Associative LSTM","full_name":"Associative LSTM"}],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1602.03032","atlas_url":"https://app.syntology.ai/?focus=1602.03032","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}