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Synopsys Physical Compiler

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Preview of Mitigating Catastrophic Forgetting in Reinforcement Learning
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Mitigating Catastrophic Forgetting in Reinforcement Learning (1802.07239.pdf)

2.49 MBChristos Kaplanis, Murray Shanahan, Claudia ClopathEN 14 pages
Unlike humans, artificial neural networks suffer from catastrophic forgetting, where new learning erases previously acquired knowledge. We show that a synaptic model with biological complexity can mitigate this issue at multiple timescales. This enables continual learning across...
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learning catastrophic forgetting mitigating reinforcement humans artificial neural networks suffer erases acquired knowledge synaptic model biological complexity mitigate issue multiple timescales enables continual sequential training tasks

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