Multi-task learning is a way of learning multiple tasks simultaneously with a shared model or representation. For example, you can train a model that can perform both sentiment analysis and topic ...

[MGDA-UB] Sener, O., & Koltun, V. Multi-task learning as multi-objective optimization. NeurIPS, 2018. Notes: cast multi-task learning as multi-objective optimization; provide an upper bound for the ...

Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to leverage useful information contained in multiple related tasks to help improve the generalization performance of ...

Multi-task learning (MTL) is a machine learning approach. It trains a single model on multiple tasks at once. Learning shared representations across related tasks can boost performance, generalisation ...

AdaShare is a novel and differentiable approach for efficient multi-task learning that learns the feature sharing pattern to achieve the best recognition accuracy, while restricting the memory ...

Definition 2 (Multi-task learning). Given multiple related learning tasks, the goal of multi-task learning is to improve the performance of each task by jointly learning these related tasks and mining ...

When to multi-task and when to single-task. Nobody is limited to exactly one category: we are all multi-taskers at times, while other times we have the time and availability to focus on just one task.

If you take a close look at your surroundings, then you will notice everything has something that is powered by AI, from ...