Papers with MLC task

1 papers
A Deep Reinforced Sequence-to-Set Model for Multi-Label Classification (P19-1)

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Challenge: Multi-label classification (MLC) aims to assign multiple labels to each sample.
Approach: They propose a sequence-to-set model that is trained via reinforcement learning and rewards feedback independent of the label order.
Outcome: The proposed model outperforms baseline models and reduces sensitivity to label order.

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