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• W2560647685 abstract "Significance Deep neural networks are currently the most successful machine-learning technique for solving a variety of tasks, including language translation, image classification, and image generation. One weakness of such models is that, unlike humans, they are unable to learn multiple tasks sequentially. In this work we propose a practical solution to train such models sequentially by protecting the weights important for previous tasks. This approach, inspired by synaptic consolidation in neuroscience, enables state of the art results on multiple reinforcement learning problems experienced sequentially." @default.
• W2560647685 created "2016-12-16" @default.
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• W2560647685 date "2017-03-14" @default.
• W2560647685 modified "2023-10-17" @default.
• W2560647685 title "Overcoming catastrophic forgetting in neural networks" @default.
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• W2560647685 doi "https://doi.org/10.1073/pnas.1611835114" @default.
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