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As deep learning applications continue to become more diverse, an interestingquestion arises: Can general problem solving arise from jointly learningseveral such diverse tasks? To approach this question, deep multi-task learningis extended in this paper to the setting where there is no obvious overlapbetween task architectures. The idea is that any set of (architecture,task)pairs can be decomposed into a set of potentially related subproblems, whosesharing is optimized by an efficient stochastic algorithm. The approach isfirst validated in a classic synthetic multi-task learning benchmark, and thenapplied to sharing across disparate architectures for vision, NLP, and genomicstasks. It discovers regularities across these domains, encodes them intosharable modules, and combines these modules systematically to improveperformance in the individual tasks. The results confirm that sharing learnedfunctionality across diverse domains and architectures is indeed beneficial,thus establishing a key ingredient for general problem solving in the future.

Modular Universal Reparameterization: Deep Multi-task Learning Across Diverse Domains