Deep Kernel Learning

6 Nov 2015Andrew Gordon WilsonZhiting HuRuslan SalakhutdinovEric P. Xing

We introduce scalable deep kernels, which combine the structural properties of deep learning architectures with the non-parametric flexibility of kernel methods. Specifically, we transform the inputs of a spectral mixture base kernel with a deep architecture, using local kernel interpolation, inducing points, and structure exploiting (Kronecker and Toeplitz) algebra for a scalable kernel representation... (read more)

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