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Adaptive control of recurrent neural networks using conceptors
In a Machine Learning setting, the network's parameters are adapted during a training phase to match the requirements of a given task/problem increasing its computational capabilities.
How to reduce computation time while sparing performance during robot navigation? A neuro-inspired architecture for autonomous shifting between model-based and model-free learning
We present a novel arbitration mechanism between learning systems that explicitly measures performance and cost.
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