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A Closed-Loop Evaluation of Capability Loss and Recovery in Compressed Driving Policies
Many automobile and mobility companies deploy learned driving policies on embedded computers with limited memory and power. Pruning, knowledge distillation, and quantization are the standard methods to reduce the size and the inference cost of these policies. However, these methods are commonly asse
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