Question about SNGP differences from the paper

Hi. Thanks for keeping everything updated here. I noticed there are some differences between the SNGP implementation here and what is described in the paper which leaves a few questions I am curious about:

- The default settings here: https://github.com/google/uncertainty-baselines/blob/main/baselines/cifar/sngp.py#L155 place the ridge penalty at `1` instead of `1e-3` as mentioned in table 5 of the paper. Is this a result of using the Gaussian likelihood instead of the logistic?
- In conjunction with the point above, there seems to be an extra multiplication which is not included in the paper equations in the predictive variance (https://github.com/google/edward2/blob/main/edward2/tensorflow/layers/random_feature.py#L456) which multiplies by the ridge penalty again after inversion. Was this used in the original experiments? How is this justified?
- Regarding this comment: https://github.com/google/uncertainty-baselines/issues/258, it says that the current code used the Gaussian likelihood for simplification, but in the paper it seems to follow a one vs all logistic regression. Was a one-vs-all logistic regression used in the original training? or was it always a softmax even though the likelihood is logistic, or something else entirely?