Minimum Probability Flow Learning — mentioned by andbberger

Thu, 02 Jul 2015 06:15:56 GMT

Virtually everyone uses gradient based methods in the end to fine tune the weights. Yes, there are other methods. Contrastive divergence seems to be king right now - of note is Minimum probability flow learning [1] (of which CD is a special case of). However the flavor of these methods tends to be tuning the weights of the model in such a way to maximize how close the model comes to sharing the probability distribution of the data. One can generally not constraint the model parameters (ie by freezing a layer) and retain the models ability to 'learn' the data distribution.…

Paper and discussion history. A mention is not an endorsement.

Minimum Probability Flow Learning · HackerPapers discussions

Minimum Probability Flow Learning — mentioned by andbberger