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Dropout is computationally expensive though. It does address overfitting (by doing something like training an ensemble of weaker models, none of which will indi
by zozbot123 8y ago
Dropout is computationally expensive though. It does address overfitting (by doing something like training an ensemble of weaker models, none of which will individually overfit, but all of which capture different "views" of the dataset), but it's not without its drawbacks, and people seem to be using it less these days.
- sdenton4 8y agoQua? I was under the impression that it was just going into architecture with less remark - ubiquitous, rather than dying. It's a great step towards building sparse models, as well, which is important for client side deployments.