… In Figure 4 we show the spectral density (the Fourier transform) of the base kernels learned
through our deep kernel learning method. The expressive spectral mixture (SM) kernel …

… Deep kernel learning and related techniques promise to … the overfitting from overparameterized
deep kernel learning, in … fully Bayesian treatment of deep kernel learning can rectify this …

… kernel methods with the inductive biases of deep learning architectures. We propose a novel
deep kernel learning … deep kernel learning approaches to enable classification, multi-task …

… learning in the deep kernel learning framework where test-time adaptation is achieved through
kernel … We also create another layer of adaptation by allowing task-specific kernels that …

… of deep kernel methods (or deep kernel learning), we choose to focus on the deep MKL …
shown great success in automated kernel learning and optimization for kernel methods. The rest …

… A supervised learning of convolutional kernel … ], our deep kernel networks, proposed in this
paper, learn implicit mapping functions using nonlinear combinations of elementary kernels. …

… Deep models, which are typically heavily over-parametrized, tend to fit the training … deep
learning. Using six realworld and two synthetic datasets, we establish experimentally that kernel …

… how to use kernel methods in deep architectures [7]. We show how to train deep kernel-based …
Using the arc-cosine kernels in the previous section, these multilayer kernel machines (…

… deep kernel learning can be constructed in section 3. Section 4 exhibits the combination of
kernel methods and deep … paper and gives the outlook of future work in deep kernel learning. …

… deep kernel algorithms have been presented to try to link kernel method with deep learning.
… A representer theorem for deep kernel learning. arXiv preprint arXiv:1709.10441, 2017. […