Appendix A.1 defines the augmented state-space model that formalizes the dynamics of the Gauss-Markov processes introduced in Section 3.1. Appendix A.2 provides the equations for prediction and update steps of the extended Kalman filter in such a setup, which is The block-diagonal structure is due to the independent dynamics of the prior processes. In the experiments presented in Sections 5.2 and 5.3 we model the latent contact rate This section is concerned with the exact steps that make up the algorithm summarized in Section 3.4. The stochastic differential equation defined in Eq. As detailed in Section 3, two different update steps are defined for two kinds of observations.

Equation (3); uses our comprehensive step-size search for updates. Importance-weighted training is used with M = 10 (optimizes IW-ELBO with M = 10). Importance-weighted sampling is not used; importance-weighted training is not used. Importance-weighted sampling is not used; importance-weighted training is not used. Importance-weighted training is used with M = 10.

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As a result we can learn a kernel that transfers to new tasks; we call this Deep Kernel Transfer (DKT) .

We survey the current practice in evaluation and show that the techniques we recommend are rarely used in practice.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The goal of this paper is to build a markov chain that will sample from a determinantal point process. One that mixes rapidly, and improves on the O(n^3) direct computation. One benefit is that as the set of elements in the DPP changes, there is no expensive eigenvalue decomposition. The fast algorithm is achieved with the realization that the algorithm doesn't require the computation of matrix determinants, but the ratio of determinants.