---
res:
  bibo_abstract:
  - 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.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Georg S
      foaf_name: Martius, Georg S
      foaf_surname: Martius
      foaf_workInfoHomepage: http://www.librecat.org/personId=3A276B68-F248-11E8-B48F-1D18A9856A87
  - foaf_Person:
      foaf_givenName: Christoph
      foaf_name: Lampert, Christoph
      foaf_surname: Lampert
      foaf_workInfoHomepage: http://www.librecat.org/personId=40C20FD2-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0001-8622-7887
  dct_date: 2017^xs_gYear
  dct_language: eng
  dct_publisher: International Conference on Learning Representations@
  dct_title: Extrapolation and learning equations@
...