AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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The efficacy of the proposed heterogeneous kernel metrics for Deep Kernel Learning is substantiated through experimental studies on a 1/10th scale racecar platform, demonstrating improved prediction accuracy and thereby safely overtaking against OVs.
This work proposes a continual learning framework for traversability prediction that incrementally adapts to new terrains using a generative experience recall model, and incorporates the uncertainty of the generated samples from the recall model, enabling uncertainty-aware adaptation.
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