Deep kernel machines (DKMs) are a recently introduced kernel method with the
flexibility of other deep models including deep NNs and deep Gaussian
processes. DKMs work purely with kernels, never with features, and are
therefore different from other methods ranging from NNs to deep kernel learning
and even deep Gaussian processes, which all use features as a fundamental
component. Here, we introduce convolutional DKMs, along with an efficient
inter-domain inducing point approximation scheme. Further, we develop and
experimentally assess a number of model variants, including 9 different types
of normalisation designed for the convolutional DKMs, two likelihoods, and two
different types of top-layer. The resulting models achieve around 99% test
accuracy on MNIST, 92% on CIFAR-10 and 71% on CIFAR-100, despite training in
only around 28 GPU hours, 1-2 orders of magnitude faster than full NNGP / NTK /
Myrtle kernels, whilst achieving comparable performance.

深度核机器（DKMs）是一种最近引入的核方法，具有包括深度神经网络和深高斯过程在内的其他深度模型的灵活性。本文介绍了卷积 DKMs 以及一种高效的跨域引导点逼近方案，并且开发和实验评估了多种模型变体，包括针对卷积 DKMs 设计的 9 种不同类型的规范化方法、两种似然函数和两种不同类型的顶层。尽管训练时间只需要大约 28 个 GPU 小时，比完整的 NNGP/NTK/Myrtle 核函数快 1 到 2 个数量级，但结果模型在 MNIST 上达到约 99% 的测试准确率，在 CIFAR-10 上达到 92% 的准确率，在 CIFAR-100 上达到 71% 的准确率，同时实现了可比较的性能。