With Sobolev Training, neural networks are trained to fit target output values as well as target derivatives with respect to the inputs. This leads to better generalization and fewer required training examples for certain problems. In this paper, we present a training pipeline that enables Sobolev Training for regression problems where target derivatives are not directly available. Thus, we propose to use a least-squares estimate of the target derivatives based on function values of neighboring training samples. We show for a variety of black-box function regression tasks that our training pipeline achieves smaller test errors compared to the traditional training method. Since our method has no additional requirements on the data collection process, it has great potential to improve the results for various regression tasks.      «

With Sobolev Training, neural networks are trained to fit target output values as well as target derivatives with respect to the inputs. This leads to better generalization and fewer required training examples for certain problems. In this paper, we present a training pipeline that enables Sobolev Training for regression problems where target derivatives are not directly available. Thus, we propose to use a least-squares estimate of the target derivatives based on function values of neighboring...     »