From: ~Ransalu_Senanayake1
comment: This paper describes a method for learning a model for a dynamic system based on the Stochastic differential equations (SDE). The deterministic driving function and stochastic diffusion components of the SDE are modeled using Gaussian processes. The main novelty in this paper is including a time component in the former function. 

    1. What did you learn from reading this paper that you did not know before?
 The paper introduces and explains the SDE model very well. The concept of using inducing variables was also new to the reviewer, though could use some more explanation. 
    2. What was the most exciting part of the work?
 The most exciting part of this work is that the model is very general and seems to lend itself well towards including inputs, controls, etc. 
    3. What in your opinion makes this work stand apart from other papers in this area?
Rather than using a nonstationary kernel model, which suffer from nonconvexity in hyperparameter optimization and from expanding kernel dictionary, this paper’s model is based on a stationary kernel, yet seems to handle complexity and stochastic behavior well
    4. Does the paper fit into the scope of the workshop?
The paper definitely fits into the scope of this workshop.
    5. Are you aware of relevant literature that you may want to make the authors aware of?
 There is a lot of work out there using kernel methods for modeling spatiotemporally evolving systems, but the authors seem to have a good handle on it and nothing I can recall seems to jump out as particularly relevant.
title: From: NIPS 2018 Workshop Spatiotemporal Paper36 AnonReviewer1