@inproceedings{SahooLampertMartius2018:EQLDiv,
  title = {Learning equations for extrapolation and control},
  booktitle = {Proc. \textbackslash 35th International Conference on Machine Learning, {ICML} 2018, Stockholm, Sweden, 2018},
  abstract = {We present an approach to identify concise equations from data using a
  shallow neural network approach. In contrast to ordinary black-box
  regression, this approach allows understanding functional relations and
  generalizing them from observed data to unseen parts of the parameter
  space. We show how to extend the class of learnable equations for a
  recently proposed equation learning network to include divisions, and we
  improve the learning and model selection strategy to be useful for
  challenging real-world data. For systems governed by analytical
  expressions, our method can in many cases identify the true underlying
  equation and extrapolate to unseen domains. We demonstrate its
  effectiveness by experiments on a cart-pendulum system, where only 2
  random rollouts are required to learn the forward dynamics and
  successfully achieve the swing-up task.},
  volume = {80},
  pages = {4442--4450},
  howpublished = {http://proceedings.mlr.press/v80/sahoo18a/sahoo18a.pdf},
  editors = {Dy, Jennifer and Krause, Andreas},
  publisher = {{PMLR}},
  year = {2018},
  slug = {sahoolampertmartius2018-eqldiv},
  author = {Sahoo, Subham S. and Lampert, Christoph H. and Martius, Georg},
  url = {http://proceedings.mlr.press/v80/sahoo18a.html}
}