Designing models that produce accurate predictions is the fundamental objective of machine learning. This work presents methods demonstrating that when the derivatives of target variables with respect to inputs can be extracted from processes of interest, they can be leveraged to improve the accuracy of differentiable machine learning models. Four key ideas are explored: (1) Improving the predictive accuracy of linear regression models and feed-forward neural networks (NNs); (2) Using the difference between the performance of feedforward NNs trained with and without gradient information to tune NN complexity (in the form of hidden node number); (3) Using gradient information to regularise linear regression; and (4) Using gradient information to improve generative image models. Across this variety of applications, gradient information is shown to enhance each predictive model, demonstrating its value for a variety of applications.

翻译：设计能产生准确预测的模型是机器学习的基本目标。这项工作提出的方法表明,当从感兴趣的过程提取投入目标变量衍生物时,可以利用这些变量来提高不同机器学习模型的准确性。探索了四个关键想法:(1) 提高线性回归模型和饲料向神经网络的预测准确性;(2) 利用受过训练的有梯度信息和非梯度信息向非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非特非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非非