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Training machine learning models using task selection policies to increase learning progress

机译：使用任务选择策略培训机器学习模型来增加学习进度

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Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a machine learning model. In one aspect, a method includes receiving training data for training the machine learning model on a plurality of tasks, where each task includes multiple batches of training data. A task is selected in accordance with a current task selection policy. A batch of training data is selected from the selected task. The machine learning model is trained on the selected batch of training data to determine updated values of the model parameters. A learning progress measure that represents a progress of the training of the machine learning model as a result of training the machine learning model on the selected batch of training data is determined. The current task selection policy is updated using the learning progress measure.
机译：方法，系统和设备，包括在计算机存储介质上编码的计算机程序，用于训练机器学习模型。在一个方面，一种方法包括接收用于在多个任务上训练机器学习模型的训练数据，其中每个任务包括多个批量训练数据。根据当前任务选择策略选择任务。从所选任务中选择了一批训练数据。机器学习模型在所选批量培训数据上培训以确定模型参数的更新值。确定了作为在所选批量训练数据上训练机器学习模型的机器学习模型的培训进度的学习进度测量。使用学习进度测量更新当前的任务选择策略。

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