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аналитика - Multi-task learning
Efficient Multi-Task Optimization through Adaptive Group-Based Task Alignment
Aligning learning progress across tasks through adaptive group-based task interactions to improve overall multi-task performance while maintaining computational efficiency.
Multi-Task Learning with Robust Regularized Clustering for Outlier Task Detection
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.
Leveraging Cross-Task Relationships for Efficient Multi-Task Learning with Partial Supervision
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.
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