NeurIPS 2020

Stationary Activations for Uncertainty Calibration in Deep Learning

Meta Review

The authors proposes activation functions derived from stationary Matern family kernels which is widely used in Gaussian Process and can approximate uncertainty. Reviewers found that the paper to be well motivated and clearly described in the context of previous related work. This could be further improved by expanding the discussion to other GP kernels similar to Matern and the reason for the specific choice of Matern in that larger context. The empirical results were adequate but could be improved. The Fig 1 and 2 need further elaboration and analysis to explain the anomalies pointed out by Reviewer #4. There were additional concerns about experimental parameters and replicability. See comments from Reviewer #4.