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Multitask Learning

Multitask Learning (MTL) is a machine learning paradigm in which a model is trained simultaneously on multiple related tasks, sharing representations across them to improve generalization. Introduced formally by Rich Caruana in 1997, MTL draws on the intuition that auxiliary tasks act as inductive bias, providing extra supervision signals that help the shared layers learn richer, more robust feature representations than single-task training would yield.

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Multitask Learning

Rekod kaedah taksonomik · ml-model / deep-learning

Caruana, R. (1997). Multitask learning. Machine Learning, 28(1), 41–75. · DOI 10.1023/A:1007379606734

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Taxonomic bucketCurriculum Learningmachine-suggested · Relational suggestion, not evidence.Same method familyKnowledge Distillationmachine-suggested · Relational suggestion, not evidence.Same method familyTransfer Learningmachine-suggested · Relational suggestion, not evidence.