Training Neural Networks as Learning Data-adaptive Kernels: Provable Representation and Approximation Benefits
神经网络训练作为数据自适应核学习：表示与逼近收益
Consider the problem: given the data pair (x,y) drawn from a population with f*(x)=E[y|x=x], specify a neural network model and run gradient flow on the weights over time until reaching any stationarity. How does ft , the function computed by the neural network at time t, relate to f*, in terms of approximation and representation? What are the provable benefits of the adaptive representation by neural networks compared to the pre-specified fixed basis representation in the…