How is deviance defined in H2O Deep Learning if the loss function is not quadratic?

One can set the parameter “stopping_metric” to “deviance” for the deeplearning algorithm (http://docs.h2o.ai/h2o/latest-stable/h2o-py/docs/modeling.html#h2o.estimators.deeplearning.H2ODeepLearningEstimator.stopping_metric).

In the FAQs (http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/deep-learning.html), the answer to the question “How is deviance computed for a Deep Learning regression model?” is given by the following formula:

Loss = Quadratic -> MSE==Deviance For Absolute/Laplace or Huber -> MSE != Deviance.

As far as I understand, the deviance equals the MSE if the loss function is quadratic. How is it defined in the other cases (e.g. Huber)? I read the definition that in this case the deviance is not the MSE but what is it then?

The code seems to be here:

https://github.com/h2oai/h2o-3/blob/5403e7c7d1e787013105ca977c1a62c249b8ed61/h2o-core/src/main/java/hex/Distribution.java#L69

case huber:
if (Math.abs(y-f) <= huberDelta) {
return w * (y - f) * (y - f); // same as wMSE
} else {
return 2 * w * (Math.abs(y-f) - huberDelta)*huberDelta; // note quite the same as wMAE
}

I.e. if the error is less than huberDelta then it is MSE, otherwise it is a kind of MAE.

E.g. if y (correct value) is 0.5, f (the prediction) is 0.7, and huberDelta is 0.05,(and w is 1.0, for simplicity), MSE would be 0.04, but Huber deviance will be 2 * 0.15 * 0.05 = 0.015. If f was 0.6, MSE would be 0.01, while Huber deviance would be 2 * 0.05 * 0.05 = 0.005.

double huberDelta = MathUtils.computeWeightedQuantile(fTrain.vec(get_params()._weights_column), absdiff, get_params()._huber_alpha);

Huber Alpha, in turn, can be between 0.0 and 1.0, but defaults to 0.9: http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/algo-params/huber_alpha.html