BriefGPT.xyz
Mar, 2019
回归中的无害噪声数据插值
Harmless interpolation of noisy data in regression
HTML
PDF
Vidya Muthukumar, Kailas Vodrahalli, Anant Sahai
TL;DR
本研究探讨了深度神经网络在训练数据含有噪声且参数个数超过数据点个数时，仍能够实现零训练误差且具有泛化能力的机制，并阐述了过拟合和特征选择不佳对泛化能力的影响。
Abstract
A continuing mystery in understanding the empirical success of
deep neural networks
has been in their ability to achieve
zero training error
and yet generalize well, even when the training data is noisy and there
→