{"project":[{"name":"Lifelong Learning of Visual Scene Understanding","call_identifier":"FP7","_id":"2532554C-B435-11E9-9278-68D0E5697425","grant_number":"308036"}],"main_file_link":[{"url":"https://arxiv.org/abs/1610.02995","open_access":"1"}],"quality_controlled":"1","title":"Extrapolation and learning equations","date_published":"2017-02-21T00:00:00Z","citation":{"short":"G.S. Martius, C. Lampert, in:, 5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings, International Conference on Learning Representations, 2017.","mla":"Martius, Georg S., and Christoph Lampert. “Extrapolation and Learning Equations.” <i>5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings</i>, International Conference on Learning Representations, 2017.","ama":"Martius GS, Lampert C. Extrapolation and learning equations. In: <i>5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings</i>. International Conference on Learning Representations; 2017.","ieee":"G. S. Martius and C. Lampert, “Extrapolation and learning equations,” in <i>5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings</i>, Toulon, France, 2017.","chicago":"Martius, Georg S, and Christoph Lampert. “Extrapolation and Learning Equations.” In <i>5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings</i>. International Conference on Learning Representations, 2017.","apa":"Martius, G. S., &#38; Lampert, C. (2017). Extrapolation and learning equations. In <i>5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings</i>. Toulon, France: International Conference on Learning Representations.","ista":"Martius GS, Lampert C. 2017. Extrapolation and learning equations. 5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings. ICLR: International Conference on Learning Representations."},"year":"2017","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","ec_funded":1,"month":"02","department":[{"_id":"ChLa"}],"day":"21","publisher":"International Conference on Learning Representations","conference":{"start_date":"2017-04-24","location":"Toulon, France","name":"ICLR: International Conference on Learning Representations","end_date":"2017-04-26"},"author":[{"id":"3A276B68-F248-11E8-B48F-1D18A9856A87","full_name":"Martius, Georg S","last_name":"Martius","first_name":"Georg S"},{"full_name":"Lampert, Christoph","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-8622-7887","first_name":"Christoph","last_name":"Lampert"}],"status":"public","external_id":{"arxiv":["1610.02995"]},"date_created":"2019-09-01T22:01:00Z","abstract":[{"text":"In classical machine learning, regression is treated as a black box process of identifying a suitable function from a hypothesis set without attempting to gain insight into the mechanism connecting inputs and outputs. In the natural sciences, however, finding an interpretable function for a phenomenon is the prime goal as it allows to understand and generalize results. This paper proposes a novel type of function learning network, called equation learner (EQL), that can learn analytical expressions and is able to extrapolate to unseen domains. It is implemented as an end-to-end differentiable feed-forward network and allows for efficient gradient based training. Due to sparsity regularization concise interpretable expressions can be obtained. Often the true underlying source expression is identified.","lang":"eng"}],"date_updated":"2021-01-12T08:09:17Z","oa_version":"Preprint","type":"conference","_id":"6841","oa":1,"scopus_import":1,"publication":"5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings","language":[{"iso":"eng"}],"publication_status":"published"}