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by andbberger
4000 days ago
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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. [1]http://arxiv.org/abs/0906.4779 |
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