text: The authors propose an extension to CNN using an autoregressive weighting for asynchronous time series applications. The method is applied to a proprietary dataset as well as a couple UCI problems and a synthetic dataset, showing improved performance over baselines in the asynchronous setting. This paper is mostly an applications paper. The method itself seems like a fairly simple extension for a particular application, although perhaps the authors have not clearly highlighted details of methodological innovation. I liked that the method was motivated to solve a real problem, and that it does seem to do so well compared to reasonable baselines. However, as an an applications paper, the bread of experiments are a little bit lacking -- with only that one potentially interesting dataset, which happens to proprietary. Given the fairly empirical nature of the paper in general, it feels like a strong argument should be made, which includes experiments, that this work will be generally significant and impactful. The writing of the paper is a bit loose with comments like: “Besides these and claims of secretive hedge funds (it can be marketing surfing on the deep learning hype), no promising results or innovative architectures were publicly published so far, to the best of our knowledge.” Parts of the also appear rush written, with some sentences half finished: “"ues of x might be heterogenous, hence On the other hand, significance network provides data-dependent weights for all regressors and sums them up in autoregressive manner.”” As a minor comment, the statement “however, due to assumed Gaussianity they are inappropriate for financial datasets, which often follow fat-tailed distributions (Cont, 2001).” Is a bit too broad. It depends where the Gaussianity appears. If the likelihood is non-Gaussian, then it often doesn’t matter if there are latent Gaussian variables.