Universum Prescription: Regularization using Unlabeled Data — mentioned by kastnerkyle

Sun, 22 Nov 2015 21:33:29 GMT

X. Zhang and Y. LeCun had an article [1] about using this technique for regularization very recently. We aren't talking specifically about overfitting in this case, but rather a lack of diversity / size in the training data, however it seems like this kind of thing might help for a variety of tasks. One more idea would be to have a (non-differentiable / REINFORCE?) penalty based on sentence likelihood using doc2vec or skip-thoughts to avoid the "blah and a blah on a blah" type errors that seem to be common in captioning. One more would be to use TV / YouTube captions, but that data is…

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

Universum Prescription: Regularization using Unlabeled Data · HackerPapers discussions

Universum Prescription: Regularization using Unlabeled Data — mentioned by kastnerkyle