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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 45814590 of 9051 papers

TitleStatusHype
Beyond Attentive Tokens: Incorporating Token Importance and Diversity for Efficient Vision TransformersCode0
Evolutionary Strategies for the Design of Binary Linear Codes0
Plug and Play Active Learning for Object DetectionCode1
Automating Rigid Origami DesignCode1
MEESO: A Multi-objective End-to-End Self-Optimized Approach for Automatically Building Deep Learning Models0
Cultural Incongruencies in Artificial Intelligence0
A 2030 United States Macro Grid Unlocking Geographical Diversity to Accomplish Clean Energy Goals0
An Empirical Study On Contrastive Search And Contrastive Decoding For Open-ended Text GenerationCode1
EDGE: Editable Dance Generation From MusicCode2
ArtELingo: A Million Emotion Annotations of WikiArt with Emphasis on Diversity over Language and Culture0
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