{"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/understanding-hidden-memories-of-recurrent","title":"Understanding Hidden Memories of Recurrent Neural Networks","arxiv_id":"1710.10777","date":"2017-10-30","proceeding":null,"authors":["Yao Ming","Shaozu Cao","Ruixiang Zhang","Zhen Li","Yuanzhe Chen","Yangqiu Song","Huamin Qu"],"abstract":"Recurrent neural networks (RNNs) have been successfully applied to various\nnatural language processing (NLP) tasks and achieved better results than\nconventional methods. However, the lack of understanding of the mechanisms\nbehind their effectiveness limits further improvements on their architectures.\nIn this paper, we present a visual analytics method for understanding and\ncomparing RNN models for NLP tasks. We propose a technique to explain the\nfunction of individual hidden state units based on their expected response to\ninput texts. We then co-cluster hidden state units and words based on the\nexpected response and visualize co-clustering results as memory chips and word\nclouds to provide more structured knowledge on RNNs' hidden states. We also\npropose a glyph-based sequence visualization based on aggregate information to\nanalyze the behavior of an RNN's hidden state at the sentence-level. The\nusability and effectiveness of our method are demonstrated through case studies\nand reviews from domain experts.","url_abs":"http://arxiv.org/abs/1710.10777v1","url_pdf":"http://arxiv.org/pdf/1710.10777v1.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":"understanding-hidden-memories-of-recurrent","repo_url":"https://github.com/myaooo/RNNVis","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"sentence","task_name":"Sentence"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1710.10777","atlas_url":"https://app.syntology.ai/?focus=1710.10777","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}