{"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/interpretable-recurrent-neural-networks-using","title":"Interpretable Recurrent Neural Networks Using Sequential Sparse Recovery","arxiv_id":"1611.07252","date":"2016-11-22","proceeding":null,"authors":["Scott Wisdom","Thomas Powers","James Pitton","Les Atlas"],"abstract":"Recurrent neural networks (RNNs) are powerful and effective for processing\nsequential data. However, RNNs are usually considered \"black box\" models whose\ninternal structure and learned parameters are not interpretable. In this paper,\nwe propose an interpretable RNN based on the sequential iterative\nsoft-thresholding algorithm (SISTA) for solving the sequential sparse recovery\nproblem, which models a sequence of correlated observations with a sequence of\nsparse latent vectors. The architecture of the resulting SISTA-RNN is\nimplicitly defined by the computational structure of SISTA, which results in a\nnovel stacked RNN architecture. Furthermore, the weights of the SISTA-RNN are\nperfectly interpretable as the parameters of a principled statistical model,\nwhich in this case include a sparsifying dictionary, iterative step size, and\nregularization parameters. In addition, on a particular sequential compressive\nsensing task, the SISTA-RNN trains faster and achieves better performance than\nconventional state-of-the-art black box RNNs, including long-short term memory\n(LSTM) RNNs.","url_abs":"http://arxiv.org/abs/1611.07252v1","url_pdf":"http://arxiv.org/pdf/1611.07252v1.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":"interpretable-recurrent-neural-networks-using","repo_url":"https://github.com/stwisdom/sista-rnn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"compressive-sensing","task_name":"Compressive Sensing"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}