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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

Showing 19311940 of 9051 papers

TitleStatusHype
Audio-to-Image Cross-Modal Generation0
Alibaba Submission to the WMT20 Parallel Corpus Filtering Task0
Creative Preference Optimization0
Creativity Has Left the Chat: The Price of Debiasing Language Models0
Conformity bias in the cultural transmission of music sampling traditions0
Configuring Antenna System to Enhance the Downlink Performance of High Velocity Users in 5G MU-MIMO Networks0
Assessing fish abundance from underwater video using deep neural networks0
Audio-Visual Segmentation via Unlabeled Frame Exploitation0
ConfigTron: Tackling network diversity with heterogeneous configurations0
Assessing Distractors in Multiple-Choice Tests0
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