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Found 5 papers, 2 papers with code
Less is More -- Towards parsimonious multi-task models using structured sparsity
In this work, we introduce channel-wise l1/l2 group sparsity in the shared convolutional layers parameters (or weights) of the multi-task learning model.
Multi-Task Meta Learning: learn how to adapt to unseen tasks
In particular, it focuses simultaneous learning of multiple tasks, an element of MTL and promptly adapting to new tasks, a quality of meta learning.
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Sharing to learn and learning to share -- Fitting together Meta-Learning, Multi-Task Learning, and Transfer Learning: A meta review
Integrating knowledge across different domains is an essential feature of human learning.
Mixed-norm Regularization for Brain Decoding
This framework is extended to the multi-task learning situation where several similar classification tasks related to different subjects are learned simultaneously.
Independent Vector Analysis: Identification Conditions and Performance Bounds
Thus, we provide the additional conditions for when the arbitrary ordering of the sources within each dataset is common.
