{"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/efficiency-evaluation-of-character-level-rnn","title":"Efficiency Evaluation of Character-level RNN Training Schedules","arxiv_id":"1605.02486","date":"2016-05-09","proceeding":null,"authors":["Cedric De Boom","Sam Leroux","Steven Bohez","Pieter Simoens","Thomas Demeester","Bart Dhoedt"],"abstract":"We present four training and prediction schedules from the same\ncharacter-level recurrent neural network. The efficiency of these schedules is\ntested in terms of model effectiveness as a function of training time and\namount of training data seen. We show that the choice of training and\nprediction schedule potentially has a considerable impact on the prediction\neffectiveness for a given training budget.","url_abs":"http://arxiv.org/abs/1605.02486v1","url_pdf":"http://arxiv.org/pdf/1605.02486v1.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":"efficiency-evaluation-of-character-level-rnn","repo_url":"https://github.com/cedricdeboom/CharRNN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"prediction","task_name":"Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}