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Diversity

Diversity in data sampling is crucial across various use cases, including search, recommendation systems, and more. Ensuring diverse samples means capturing a wide range of variations and perspectives, which leads to more robust, unbiased, and comprehensive models. In search use cases, for instance, diversity helps avoid redundancy, ensuring that users are exposed to a broader set of relevant information rather than repeated similar results.

Papers

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Showing 4051–4060 of 9051 papers

TitleStatusHype
Multi-Robot Coordination and Layout Design for Automated WarehousingCode1
Array-Informed Waveform Design for Active Sensing: Diversity, Redundancy, and Identifiability—0
Template-based eukaryotic genome editing directed by SviCas3—0
Generative AI meets 3D: A Survey on Text-to-3D in AIGC Era—0
Achieving Diversity in Counterfactual Explanations: a Review and Discussion—0
Robust multi-agent coordination via evolutionary generation of auxiliary adversarial attackersCode1
Learnable Behavior Control: Breaking Atari Human World Records via Sample-Efficient Behavior Selection—0
On the Limitations of Model Stealing with Uncertainty Quantification Models—0
Joint BS Selection, User Association, and Beamforming Design for Network Integrated Sensing and Communication—0
Even Small Correlation and Diversity Shifts Pose Dataset-Bias Issues—0
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