4 ms·
DNNs can be thought of a stacked Restricted Boltzmann Machines. Their structure and training is very different to traditional MLPs. They derive in some ways fro
by jph00 14y ago
DNNs can be thought of a stacked Restricted Boltzmann Machines. Their structure and training is very different to traditional MLPs. They derive in some ways from convolutional neural nets.
I describe some of the key differences between DNNs and MLPs in the webinar. Also, the webinar explains how recent advances go far beyond just applications to speech recognition - in particular I focus on a case study in chemoinformatics.
- ergodic 14y ago>DNNs can be thought of a stacked Restricted Boltzmann Machines Agree, as explained in Hinton et al 2006. http://www.cs.toronto.edu/~hinton/absps/ncfast.pdf http://www.cs.toronto.edu/~hinton/absps/ncfast.pdf But this is just for pre-training, as I said. If you look at Seides paper, they pre-train treating the MLP as a DBN and then they train it as a classic MLP with BP. Also using layer-wise BP pre-training does bring performance close to DBN pre-training, with no use of DBNs paradigms at all. >Their structure and training is very different to traditional MLPs I insist if we are talking of the same DNNs explained in Microsofts paper, this is not true. If we were to be talking about different DNNs please elaborate I would love to hear about that (seriously, no irony here).