Open Peer Review. Open Publishing. Open Access. Open Discussion. Open Directory. Open Recommendations. Open API. Open Source.
Extrapolation and learning equations
Georg Martius, Christoph H. Lampert
Feb 21, 2017 (modified: Feb 21, 2017)ICLR 2017 workshop submissionreaders: everyone
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.
TL;DR:We present the learning of analytical equation from data using a new forward network architecture.
Keywords:Supervised Learning, Deep learning, Structured prediction
Enter your feedback below and we'll get back to you as soon as possible.