2 research outputs found
End-to-end Learning, with or without Labels
We present an approach for end-to-end learning that allows one to jointly
learn a feature representation from unlabeled data (with or without labeled
data) and predict labels for unlabeled data. The feature representation is
assumed to be specified in a differentiable programming framework, that is, as
a parameterized mapping amenable to automatic differentiation. The proposed
approach can be used with any amount of labeled and unlabeled data, gracefully
adjusting to the amount of supervision. We provide experimental results
illustrating the effectiveness of the approach