Precise asymptotic analysis of Sobolev training for random feature models

A recent study delves into Sobolev training, which incorporates both function and gradient data in neural network training. This approach is particularly relevant as it addresses the generalization error in highly overparameterized models operating in high-dimensional spaces. Understanding the implications of this training method could enhance the performance of predictive models, making it a significant area of research in machine learning.