Multi-task learning is a powerful method for solving multiple correlated tasks simultaneously. 12/30/2019 ∙ by Xi Lin, et al. P. 434-441. Multi-Task Learning as Multi-Objective Optimization Ozan Sener Intel Labs Vladlen Koltun Intel Labs Abstract In multi-task learning, multiple tasks are solved jointly, sharing inductive bias between them. NeurIPS (#1, #2), ICLR (#1, #2), and ICML (#1, #2), it is very likely that a recording exists of the paper author’s presentation. A common compromise is to optimize a proxy objective that minimizes a weighted linear combination of per-task losses. However, the multi-task setting presents a number of optimization challenges, making it difficult to realize large efficiency gains compared to learning tasks independently. However, it is often impossible to find one single solution to optimize all the tasks, since different tasks might conflict with each other. Pareto Multi-Task Learning. We will use $ROOT to refer to the root folder where you want to put this project in. However, it is often impossible to find one single solution to optimize all the tasks, since different tasks might conflict with each other. We provide an example for MultiMNIST dataset, which can be found by: First, we run weighted sum method for initial Pareto solutions: Based on these starting solutions, we can run our continuous Pareto exploration by: Now you can play it on your own dataset and network architecture! However, it is often impossible to find one single solution to optimize all the tasks, since different tasks might conflict with each other. Learn more. In this paper, we propose a regularization approach to learning the relationships between tasks in multi-task learning. Multi-Task Learning (Pareto MTL) algorithm to generate a set of well-representative Pareto solutions for a given MTL problem. download the GitHub extension for Visual Studio. 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