Electronic Theses and Dissertations


LRN: Limitless Routing Networks for Effective Multi-task Learning





Date of Award


Document Type

Thesis (Campus Access Only)

Degree Name

Master of Science


Computer Science

Committee Chair

Xiaofei Zhang

Committee Member

Weizi Li

Committee Member

Deepak Venugopal


Multi-task learning (MTL) is a field involved with learning multiple tasks simultaneously typically through using shared model parameters. The shared representation enables generalized parameters that are task invariant and assists in learning tasks with sparse data. However, the presence of unforeseen task interference can cause one task to improve at the detriment of another. A recent paradigm constructed to tackle these types of problems is the routing network, that builds neural network architectures from a set of modules conditioned on the input instance, task, and previous output of other modules. This approach has many constraints, so we propose the Limitless Routing Network (LRN) which removes the constraints through the usage of a transformer-based router and a reevaluation of the state and action space. We also provide a simple solution to the module collapse problem and display superior accuracy performance over several MTL benchmarks compared to the original routing network.


Data is provided by the student.

Library Comment

Dissertation or thesis originally submitted to the local University of Memphis Electronic Theses & dissertation (ETD) Repository.

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