Latent Multi-task Architecture Learning

Understand

Multi-task learning (MTL) allows deep neural networks to learn from related tasks by sharing parameters with other networks.

• In practice, however, MTL involves searching an enormous space of possible parameter sharing architectures to find (a) the layers or subspaces that benefit from sharing, (b) the appropriate amount of sharing, and (c) the appropriate relative weights of the different task losses.
• Recent work has addressed each of the above problems in isolation.
• In this work we present an approach that learns a latent multi-task architecture that jointly addresses (a)--(c).