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In deep multi-task learning, weights of task-specific networks are sharedbetween tasks to improve performance on each single one. Since the question,which weights to share between layers, is difficult to answer, human-designedarchitectures often share everything but a last task-specific layer. In manycases, this simplistic approach severely limits performance. Instead, wepropose an algorithm to learn the assignment between a shared set of weightsand task-specific layers. To optimize the non-differentiable assignment and atthe same time train the differentiable weights, learning takes place via acombination of natural evolution strategy and stochastic gradient descent. Theend result are task-specific networks that share weights but allow independentinference. They achieve lower test errors than baselines and methods fromliterature on three multi-task learning datasets.

Learned Weight Sharing for Deep Multi-Task Learning by Natural Evolution Strategy and Stochastic Gradient Descent