I read it in diagonal but the paper seems to use the same DNN architecture as before. They seem to tweak the pretraining with layer-wise back-propagation (instead of full MLP-as-DBN pre-training). This does not imply anything new with respect to what I commented and the cited paper.
The only reference to differences I found is about differences between a DNN and a MaxEnt models, which is again not an argument for differences between DNNs and MLPs.
Could you point me to a concrete paragraph?, I would be happy to be mistaken in this regard.
The only reference to differences I found is about differences between a DNN and a MaxEnt models, which is again not an argument for differences between DNNs and MLPs.
Could you point me to a concrete paragraph?, I would be happy to be mistaken in this regard.