The recent discovery of the equivalence between infinitely wide neural networks (NNs) in the lazy training regime and Neural Tangent Kernels (NTKs) (Jacot et al., 2018) has revived interest in kernel methods. However, conventional wisdom suggests kernel methods are unsuitable for large samples due to their computational complexity and memory requirements. We introduce a novel random feature regression algorithm that allows us (when necessary) to scale to virtually infinite numbers of random features. We illustrate the performance of our method on the CIFAR-10 dataset.

翻译：最近发现惰性训练机制下无限宽神经网络与神经正切核之间的等价性，重新激发了人们对核方法的兴趣。然而，传统观点认为核方法由于计算复杂度和内存需求而不适用于大规模样本。我们提出了一种新颖的随机特征回归算法，该算法能够（在必要时）扩展到近乎无限的随机特征数量。我们通过在CIFAR-10数据集上展示该方法的性能。