End-to-end Learning, with or without Labels.
Year: 2019
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Authors: Corinne Jones, Vincent Roulet, Zaïd Harchaoui
Institution: University of Washington
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2996833338