{"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/a-continuum-among-logarithmic-linear-and","title":"A continuum among logarithmic, linear, and exponential functions, and its potential to improve generalization in neural networks","arxiv_id":"1602.01321","date":"2016-02-03","proceeding":null,"authors":["Luke B. Godfrey","Michael S. Gashler"],"abstract":"We present the soft exponential activation function for artificial neural\nnetworks that continuously interpolates between logarithmic, linear, and\nexponential functions. This activation function is simple, differentiable, and\nparameterized so that it can be trained as the rest of the network is trained.\nWe hypothesize that soft exponential has the potential to improve neural\nnetwork learning, as it can exactly calculate many natural operations that\ntypical neural networks can only approximate, including addition,\nmultiplication, inner product, distance, polynomials, and sinusoids.","url_abs":"http://arxiv.org/abs/1602.01321v1","url_pdf":"http://arxiv.org/pdf/1602.01321v1.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":"a-continuum-among-logarithmic-linear-and","repo_url":"https://github.com/taoketao/force_flows","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}