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Ulsan National Institute of Science and Technology (UNIST)
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Federated learning for depth estimation can be robust even in the face of overlapping data distributions among heterogeneous robots.
Structural deletion of irrelevant conditions can streamline decision trees while preserving their predictive reliability, challenging conventional methods that compromise either interpretability or accuracy.
