- A. Wilson, Zhiting Hu, R. Salakhutdinov, E. Xing. 2016. Supplementary Material : Deep Kernel Learning. Abstract: We test the ability of DKL to recover step functions, a challenging benchmark problem for kernel learning methods due to the underlying smoothness assumptions. We consider a particularly complicated step function with many discontinuities. The training data consists of 60,000 points (shown as black dots in Figure 1) and the test data contains 2,000 points, both uniformly distributed over (−1, 1). The DKL-SM model uses a DNN with an 1-1000-1000-500-50-2 architecture, which is the same as what used in the UCI regression tasks (section 5.1).
