Multi-task learning is a machine learning paradigm where a model is trained to perform multiple tasks simultaneously, leveraging shared representations and information across tasks. This approach enhances the model's ability to generalize, reduces overfitting, and improves efficiency by allowing for knowledge transfer between related tasks.
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