NeurIPS 2019
Sun Dec 8th through Sat the 14th, 2019 at Vancouver Convention Center
Paper ID:4315
Title:Modular Universal Reparameterization: Deep Multi-task Learning Across Diverse Domains

The submission is proposing a multi-task learning method based on sharing linear submodules. The proposed idea is interesting, novel, and shown to be effective. On the other hand, reviewers raised various issues about the empirical study. Authors did a good job addressing this issue in their response, and the final evaluation of all reviewers are positive. The paper is a good addition to the conference, and I recommend acceptance. Authors should add the promised experimental results in the camera-ready version.