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For questions related to feature learning (also known as representation learning), which is a set of techniques that can learn the features associated with the raw data. It is similar to feature engineering, but, in the case of feature learning, the features are learned and not handcrafted.

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Order of features learned by DNNs during training?

I'm looking for papers probing into the question of what features get learned when (or equivalently what subproblems get "solved" when) during the training process. For example, a paper showing that a …
jon_simon's user avatar
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