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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 44414450 of 9051 papers

TitleStatusHype
Determinantal consensus clustering0
Determinantal Beam Search0
BENCHAGENTS: Automated Benchmark Creation with Agent Interaction0
AI for All: Identifying AI incidents Related to Diversity and Inclusion0
Information Geometry for Maximum Diversity Distributions0
Detection and Measurement of Syntactic Templates in Generated Text0
Analyzing the Components of Distributed Coevolutionary GAN Training0
IFDID: Information Filter upon Diversity-Improved Decoding for Diversity-Faithfulness Tradeoff in NLG0
Detecting Trojaned DNNs Using Counterfactual Attributions0
Detecting Sockpuppets in Deceptive Opinion Spam0
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