Timezone: »
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.
Author Information
Subham S Sahoo (Indian Institute of Technology)
Christoph H. Lampert (IST Austria)
Georg Martius (Max Planck Institute for Intelligent Systems)
Related Events (a corresponding poster, oral, or spotlight)
-
2018 Oral: Learning equations for extrapolation and control »
Fri Jul 13th 07:50 -- 08:00 AM Room Victoria
More from the Same Authors
-
2020 Poster: On the Sample Complexity of Adversarial Multi-Source PAC Learning »
Nikola Konstantinov · Elias Frantar · Dan Alistarh · Christoph H. Lampert -
2019 Poster: Robust Learning from Untrusted Sources »
Nikola Konstantinov · Christoph H. Lampert -
2019 Poster: Towards Understanding Knowledge Distillation »
Mary Phuong · Christoph H. Lampert -
2019 Oral: Towards Understanding Knowledge Distillation »
Mary Phuong · Christoph H. Lampert -
2019 Oral: Robust Learning from Untrusted Sources »
Nikola Konstantinov · Christoph H. Lampert -
2018 Poster: Data-Dependent Stability of Stochastic Gradient Descent »
Ilja Kuzborskij · Christoph H. Lampert -
2018 Oral: Data-Dependent Stability of Stochastic Gradient Descent »
Ilja Kuzborskij · Christoph H. Lampert -
2017 Poster: PixelCNN Models with Auxiliary Variables for Natural Image Modeling »
Alexander Kolesnikov · Christoph H. Lampert -
2017 Poster: Multi-task Learning with Labeled and Unlabeled Tasks »
Anastasia Pentina · Christoph H. Lampert -
2017 Talk: Multi-task Learning with Labeled and Unlabeled Tasks »
Anastasia Pentina · Christoph H. Lampert -
2017 Talk: PixelCNN Models with Auxiliary Variables for Natural Image Modeling »
Alexander Kolesnikov · Christoph H. Lampert
