
Aligning learning progress across tasks through adaptive group-based task interactions to improve overall multi-task performance while maintaining computational efficiency.


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efficient-multi-task-optimization-through-adaptive-group-based-task-alignment


Efficient Multi-Task Optimization through Adaptive Group-Based Task Alignment


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The core message of this article is to propose a novel multi-task learning method called Multi-Task Learning via Robust Regularized Clustering (MTLRRC) that can simultaneously perform robust task clustering and outlier task detection.


multi-task-learning-with-robust-regularized-clustering-for-outlier-task-detection


Multi-Task Learning with Robust Regularized Clustering for Outlier Task Detection



Joint-Task Regularization (JTR) leverages cross-task relationships to simultaneously regularize all tasks in a single joint-task latent space, improving learning when data is not fully labeled for all tasks.


leveraging-cross-task-relationships-for-efficient-multi-task-learning-with-partial-supervision


Leveraging Cross-Task Relationships for Efficient Multi-Task Learning with Partial Supervision
