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Unifying Multi-Domain Multi-Task Learning: Tensor and Neural Network Perspectives
[article]
2016
arXiv
pre-print
Multi-domain learning aims to benefit from simultaneously learning across several different but related domains. In this chapter, we propose a single framework that unifies multi-domain learning (MDL) and the related but better studied area of multi-task learning (MTL). By exploiting the concept of a semantic descriptor we show how our framework encompasses various classic and recent MDL/MTL algorithms as special cases with different semantic descriptor encodings. As a second contribution, we
arXiv:1611.09345v1
fatcat:ao45l3bjazcmxmuqyrgeqjw3am