You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
In the mile.losses.ProbabilisticLoss, I know the first posterior distribution should close to the N(0,I), but the code may be confusing for me. Like the screenshotthe posterior_log_sigma has been cut off the first element, why is the first element still selected in the line of first_kl ?
The text was updated successfully, but these errors were encountered:
Hello! Empirically, we found that putting no constraint on the first distribution (e.g. making it match a standard normal distribution) slightly improves results. That's why there is no loss on the first posterior distribution.
In the mile.losses.ProbabilisticLoss, I know the first posterior distribution should close to the N(0,I), but the code may be confusing for me. Like the screenshotthe posterior_log_sigma has been cut off the first element, why is the first element still selected in the line of first_kl ?
The text was updated successfully, but these errors were encountered: