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Evaluating adversarial robustness in simulated cerebellum
It is well known that artificial neural networks are vulnerable to adversarial examples, in which great efforts have been made to improve the robustness.
An initial attempt of combining visual selective attention with deep reinforcement learning
We visualize and analyze the feature maps of DQN on a toy problem Catch, and propose an approach to combine visual selective attention with deep reinforcement learning.
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