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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 39613970 of 9051 papers

TitleStatusHype
Generating Diverse Hypotheses for Inductive Reasoning0
Generating Diverse Indoor Furniture Arrangements0
One Size Does Not Fit All: Generating and Evaluating Variable Number of Keyphrases0
Generating Diverse Programs with Instruction Conditioned Reinforced Adversarial Learning0
Generating Diverse Realistic Laughter for Interactive Art0
Generating Diverse Story Continuations with Controllable Semantics0
Diverse Instances-Weighting Ensemble based on Region Drift Disagreement for Concept Drift Adaptation0
An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models0
Diverse Image Style Transfer via Invertible Cross-Space Mapping0
BnSentMix: A Diverse Bengali-English Code-Mixed Dataset for Sentiment Analysis0
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