<ul data-eligibleForWebStory="true"><li>Modern deep architectures rely on large-scale datasets, leading to high computational costs.</li><li>Data selection can help reduce redundancy in datasets, improving training efficiency.</li><li>The concept of epsilon-sample cover quantifies sample redundancy based on inter-sample relationships.</li><li>RL-Selector introduces a reinforcement learning approach to data selection, outperforming existing methods in enhancing generalization performance and training efficiency.</li></ul>

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment

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