Papers by Brahim Touayouch

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Challenge:	Existing methods for multitask learning struggle with interference between tasks, especially as the number of tasks grows.
Approach:	They propose a reference-free method that minimizes the divergence between models' outputs and those of the merged model, automatically balancing task importance.
Outcome:	The proposed method outperforms existing methods on classification and generative tasks and remains robust when scaling to more tasks.