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NEURAL NETWORKS FOR SCALABLE CONTINUAL LEARNING IN DOMAINS WITH SEQUENTIALLY LEARNED TASKS

机译:具有顺序学习任务的域中的神经网络,用于在域中进行可扩展的持续学习

摘要

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for scalable continual learning using neural networks. One of the methods includes receiving new training data for a new machine learning task; training an active subnetwork on the new training data to determine trained values of the active network parameters from initial values of the active network parameters while holding current values of the knowledge parameters fixed; and training a knowledge subnetwork on the new training data to determine updated values of the knowledge parameters from the current values of the knowledge parameters by training the knowledge subnetwork to generate knowledge outputs for the new training inputs that match active outputs generated by the trained active subnetwork for the new training inputs.
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,用于使用神经网络可扩展的连续学习。其中一种方法包括接收新机器学习任务的新培训数据;在新培训数据上训练活动子网,以确定从主动网络参数的初始值确定有效网络参数的训练值,同时保持固定的知识参数的当前值;并在新培训数据上培训知识子网,通过培训知识子网来确定知识参数的当前值的知识参数的更新值,以为新的培训输入生成知识输出,以匹配由培训的活动子网生成的活动输出匹配的新培训输入用于新的培训投入。

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