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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 26712680 of 9051 papers

TitleStatusHype
Describing like humans: on diversity in image captioningCode0
Beer Organoleptic Optimisation: Utilising Swarm Intelligence and Evolutionary Computation MethodsCode0
Few-shot Image Generation via Masked DiscriminationCode0
Finer Metagenomic Reconstruction via Biodiversity OptimizationCode0
Bridging the Evaluation Gap: Leveraging Large Language Models for Topic Model EvaluationCode0
Leaping Into Memories: Space-Time Deep Feature SynthesisCode0
BEE: Metric-Adapted Explanations via Baseline Exploration-ExploitationCode0
DermDiff: Generative Diffusion Model for Mitigating Racial Biases in Dermatology DiagnosisCode0
Analysis of the first Genetic Engineering Attribution ChallengeCode0
Federated Voxel Scene Graph for Intracranial HemorrhageCode0
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