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Implicit regularization of multi-task learning and finetuning in overparameterized neural networks
Our findings hold qualitatively for a deep architecture trained on image classification tasks, and our characterization of the nested feature selection regime motivates a modification to PT+FT that we find empirically improves performance.
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