Feature Partitioning for Efficient Multi-Task Architectures.
Abstract
Multi-task learning holds the promise of less data, parameters, and time than training of separate models. We propose a method to automatically search over multi-task architectures while taking resource constraints into consideration. We propose a search space that compactly represents different parameter sharing strategies. This provides more effective coverage and sampling of the space of multi-task architectures. We also present a method for...
Paper Details
Title
Feature Partitioning for Efficient Multi-Task Architectures.
Published Date
Sep 25, 2019
Journal
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Notes
History